From 3227a70caef83bae19fc0f409274d06d1be7613e Mon Sep 17 00:00:00 2001 From: jgabry Date: Wed, 8 Nov 2023 10:22:28 -0700 Subject: [PATCH 01/19] start preparing next release --- DESCRIPTION | 4 ++-- NEWS.md | 23 ++++++++++++++++++++++- 2 files changed, 24 insertions(+), 3 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 1c60c03c..19d35480 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,7 +1,7 @@ Package: cmdstanr Title: R Interface to 'CmdStan' -Version: 0.6.1.9000 -Date: 2023-08-25 +Version: 0.7.0 +Date: 2023-11-08 Authors@R: c(person(given = "Jonah", family = "Gabry", role = c("aut", "cre"), email = "jsg2201@columbia.edu"), diff --git a/NEWS.md b/NEWS.md index 6818a803..7f22608d 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,4 +1,25 @@ -# cmdstanr 0.6.1.9000 +# cmdstanr 0.7.0 + +## Major new features + +* New `laplace` method by @jgabry in https://github.com/stan-dev/cmdstanr/pull/800 +* New `pathfinder` method by @SteveBronder in https://github.com/stan-dev/cmdstanr/pull/848 + +## Other improvements and bug fixes + +* Add missing link to diagnose method in CmdStanModel doc by @jgabry in https://github.com/stan-dev/cmdstanr/pull/833 +* Update array syntax in website vignette by @andrjohns in https://github.com/stan-dev/cmdstanr/pull/841 +* Compatibility fixes for cmdstan 2.33+ by @jgabry in https://github.com/stan-dev/cmdstanr/pull/843 +* Changed the delay behavour in wsl_installed by @martinmodrak in https://github.com/stan-dev/cmdstanr/pull/839 +* Improvemements to compile tests by @martinmodrak in https://github.com/stan-dev/cmdstanr/pull/836 +* Suggest format method after error due to old syntax by @jgabry in https://github.com/stan-dev/cmdstanr/pull/852 +* Update linux/wsl detection for install arch by @andrjohns in https://github.com/stan-dev/cmdstanr/pull/856 +* Clarifications in R-markdown vignette by @jgcolman in https://github.com/stan-dev/cmdstanr/pull/854 +* Fix handling of single-length inits for containers by @andrjohns in https://github.com/stan-dev/cmdstanr/pull/857 +* Add support/tests for exposing functions with tuples by @andrjohns in https://github.com/stan-dev/cmdstanr/pull/860 +* Add support/tests for exporting functions with complex types by @andrjohns in https://github.com/stan-dev/cmdstanr/pull/861 +* Add option for installing from release archive by @andrjohns in https://github.com/stan-dev/cmdstanr/pull/866 + # cmdstanr 0.6.1 From 77efec30d4ad2a39ea3b9392c4f3a582035673fd Mon Sep 17 00:00:00 2001 From: jgabry Date: Wed, 8 Nov 2023 10:24:19 -0700 Subject: [PATCH 02/19] Update _pkgdown.yml --- _pkgdown.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/_pkgdown.yml b/_pkgdown.yml index d3786f7d..b9f89cf3 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -102,6 +102,7 @@ reference: - CmdStanMLE - CmdStanLaplace - CmdStanVB + - CmdStanPathfinder - CmdStanGQ - CmdStanDiagnose - starts_with("fit-method") From 59327a0721544e900b6dd126b45081cbb525a29b Mon Sep 17 00:00:00 2001 From: jgabry Date: Wed, 8 Nov 2023 10:39:23 -0700 Subject: [PATCH 03/19] use print instead of summary in vignette --- vignettes/cmdstanr.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/vignettes/cmdstanr.Rmd b/vignettes/cmdstanr.Rmd index 0addfedf..83419cc1 100644 --- a/vignettes/cmdstanr.Rmd +++ b/vignettes/cmdstanr.Rmd @@ -401,7 +401,7 @@ fit_laplace <- mod$laplace( seed = 123, refresh = 1000 ) -fit_laplace$summary("theta") +fit_laplace$print("theta") mcmc_hist(fit_laplace$draws("theta"), binwidth = 0.025) ``` From ff4f7333dad3012c1c36239eb7bcc66af5f5cb76 Mon Sep 17 00:00:00 2001 From: jgabry Date: Wed, 8 Nov 2023 10:52:55 -0700 Subject: [PATCH 04/19] website for v0.7.0 --- NEWS.md | 29 +- README.md | 6 +- docs/404.html | 2 +- docs/LICENSE-text.html | 2 +- docs/LICENSE.html | 2 +- .../articles/articles-online-only/opencl.html | 155 ++-- docs/articles/cmdstanr-internals.html | 152 ++-- docs/articles/cmdstanr.html | 297 ++++--- .../cmdstanr_files/figure-html/laplace-1.png | Bin 0 -> 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docs/reference/as_draws.CmdStanMCMC.html | 124 +-- docs/reference/as_mcmc.list.html | 2 +- .../cmdstan_default_install_path.html | 2 +- docs/reference/cmdstan_default_path.html | 2 +- docs/reference/cmdstan_model-1.png | Bin 15795 -> 12728 bytes docs/reference/cmdstan_model-2.png | Bin 16009 -> 12864 bytes docs/reference/cmdstan_model-3.png | Bin 33305 -> 12713 bytes docs/reference/cmdstan_model.html | 142 ++-- docs/reference/cmdstanr-package-3.png | Bin 33305 -> 12713 bytes docs/reference/cmdstanr-package.html | 147 ++-- docs/reference/cmdstanr_example.html | 156 ++-- docs/reference/draws_to_csv.html | 10 +- docs/reference/eng_cmdstan.html | 2 +- .../reference/fit-method-cmdstan_summary.html | 228 +++--- docs/reference/fit-method-code.html | 2 +- .../fit-method-constrain_variables.html | 3 +- .../fit-method-diagnostic_summary.html | 19 +- docs/reference/fit-method-draws-1.png | Bin 72272 -> 59359 bytes docs/reference/fit-method-draws-2.png | Bin 671367 -> 641686 bytes 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104 +-- docs/reference/fit-method-time.html | 39 +- .../fit-method-unconstrain_draws.html | 3 +- .../fit-method-unconstrain_variables.html | 3 +- .../fit-method-variable_skeleton.html | 39 +- docs/reference/index.html | 22 +- docs/reference/install_cmdstan.html | 10 +- docs/reference/model-method-check_syntax.html | 18 +- docs/reference/model-method-compile.html | 16 +- docs/reference/model-method-diagnose.html | 14 +- .../model-method-expose_functions.html | 4 +- docs/reference/model-method-format.html | 37 +- .../model-method-generate-quantities.html | 10 +- docs/reference/model-method-laplace-1.png | Bin 0 -> 12841 bytes docs/reference/model-method-laplace.html | 426 ++++++++++ docs/reference/model-method-optimize-1.png | Bin 15795 -> 12728 bytes docs/reference/model-method-optimize-2.png | Bin 16009 -> 12864 bytes docs/reference/model-method-optimize-3.png | Bin 33305 -> 12713 bytes docs/reference/model-method-optimize.html | 192 +++-- docs/reference/model-method-pathfinder-1.png | 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docs/reference/set_cmdstan_path.html | 2 +- docs/reference/stan_threads.html | 2 +- docs/reference/write_stan_file.html | 20 +- docs/reference/write_stan_json.html | 14 +- docs/reference/write_stan_tempfile.html | 2 +- docs/sitemap.xml | 132 ++- 113 files changed, 3617 insertions(+), 1589 deletions(-) create mode 100644 docs/articles/cmdstanr_files/figure-html/laplace-1.png create mode 100644 docs/articles/cmdstanr_files/figure-html/plot-compare-laplace-1.png create mode 100644 docs/articles/cmdstanr_files/figure-html/plot-compare-mcmc-1.png create mode 100644 docs/articles/cmdstanr_files/figure-html/plot-compare-vb-1.png create mode 100644 docs/reference/CmdStanLaplace.html create mode 100644 docs/reference/CmdStanPathfinder.html create mode 100644 docs/reference/Rplot003.png create mode 100644 docs/reference/model-method-laplace-1.png create mode 100644 docs/reference/model-method-laplace.html create mode 100644 docs/reference/model-method-pathfinder-1.png create mode 100644 docs/reference/model-method-pathfinder-2.png create mode 100644 docs/reference/model-method-pathfinder-3.png create mode 100644 docs/reference/model-method-pathfinder.html diff --git a/NEWS.md b/NEWS.md index 7f22608d..79d1205d 100644 --- a/NEWS.md +++ b/NEWS.md @@ -2,24 +2,23 @@ ## Major new features -* New `laplace` method by @jgabry in https://github.com/stan-dev/cmdstanr/pull/800 -* New `pathfinder` method by @SteveBronder in https://github.com/stan-dev/cmdstanr/pull/848 +* New `laplace` method by @jgabry in #800 +* New `pathfinder` method by @SteveBronder in #848 ## Other improvements and bug fixes -* Add missing link to diagnose method in CmdStanModel doc by @jgabry in https://github.com/stan-dev/cmdstanr/pull/833 -* Update array syntax in website vignette by @andrjohns in https://github.com/stan-dev/cmdstanr/pull/841 -* Compatibility fixes for cmdstan 2.33+ by @jgabry in https://github.com/stan-dev/cmdstanr/pull/843 -* Changed the delay behavour in wsl_installed by @martinmodrak in https://github.com/stan-dev/cmdstanr/pull/839 -* Improvemements to compile tests by @martinmodrak in https://github.com/stan-dev/cmdstanr/pull/836 -* Suggest format method after error due to old syntax by @jgabry in https://github.com/stan-dev/cmdstanr/pull/852 -* Update linux/wsl detection for install arch by @andrjohns in https://github.com/stan-dev/cmdstanr/pull/856 -* Clarifications in R-markdown vignette by @jgcolman in https://github.com/stan-dev/cmdstanr/pull/854 -* Fix handling of single-length inits for containers by @andrjohns in https://github.com/stan-dev/cmdstanr/pull/857 -* Add support/tests for exposing functions with tuples by @andrjohns in https://github.com/stan-dev/cmdstanr/pull/860 -* Add support/tests for exporting functions with complex types by @andrjohns in https://github.com/stan-dev/cmdstanr/pull/861 -* Add option for installing from release archive by @andrjohns in https://github.com/stan-dev/cmdstanr/pull/866 - +* Add missing link to diagnose method in CmdStanModel doc by @jgabry in #833 +* Improvements to compile tests by @martinmodrak in #836 +* Changed the delay behavior in wsl_installed by @martinmodrak in #839 +* Update array syntax in website vignette by @andrjohns in #841 +* Compatibility fixes for cmdstan 2.33+ by @jgabry in #843 +* Suggest format method after error due to old syntax by @jgabry in #852 +* Clarifications in R-markdown vignette by @jgcolman in #854 +* Update linux/wsl detection for install arch by @andrjohns in #856 +* Fix handling of single-length inits for containers by @andrjohns in #857 +* Add support/tests for exposing functions with tuples by @andrjohns in #860 +* Add support/tests for exporting functions with complex types by @andrjohns in #861 +* Add option for installing from release archive by @andrjohns in #866 # cmdstanr 0.6.1 diff --git a/README.md b/README.md index 27956fec..159d0d08 100644 --- a/README.md +++ b/README.md @@ -61,9 +61,9 @@ CmdStanR_](https://mc-stan.org/cmdstanr/articles/cmdstanr.html) vignette. ### Contributing -There is a lot of work still to be done and contributions are very welcome! -If you are interested in contributing please comment on an open issue -or open a new one if none are applicable. +There is a lot of work still to be done and we welcome contributions from +anyone! If you are interested in contributing please comment on an open issue or +open a new one if none are applicable. ### License diff --git a/docs/404.html b/docs/404.html index 9dcc60be..4499f2e6 100644 --- a/docs/404.html +++ b/docs/404.html @@ -39,7 +39,7 @@ cmdstanr - 0.6.1 + 0.7.0 diff --git a/docs/LICENSE-text.html b/docs/LICENSE-text.html index 31783b5c..8f4c7007 100644 --- a/docs/LICENSE-text.html +++ b/docs/LICENSE-text.html @@ -17,7 +17,7 @@ cmdstanr - 0.6.1 + 0.7.0 diff --git a/docs/LICENSE.html b/docs/LICENSE.html index 61471785..9cfe0b94 100644 --- a/docs/LICENSE.html +++ b/docs/LICENSE.html @@ -17,7 +17,7 @@ cmdstanr - 0.6.1 + 0.7.0 diff --git a/docs/articles/articles-online-only/opencl.html b/docs/articles/articles-online-only/opencl.html index c2af01ec..af47c06b 100644 --- a/docs/articles/articles-online-only/opencl.html +++ b/docs/articles/articles-online-only/opencl.html @@ -26,6 +26,8 @@ + +
+

Running Stan on the GPU with OpenCL

-

Rok Češnovar and Jonah -Gabry

+

Rok Češnovar +and Jonah Gabry

- Source: vignettes/articles-online-only/opencl.Rmd + Source: vignettes/articles-online-only/opencl.Rmd
-
-

-Introduction

+
+

Introduction +

This vignette demonstrates how to use the OpenCL capabilities of CmdStan with CmdStanR. The functionality described in this vignette requires CmdStan 2.26.1 or newer.

@@ -160,13 +162,13 @@

using profiling, which was introduced in Stan version 2.26.0.

-
-

-OpenCL runtime

+
+

OpenCL runtime +

OpenCL is supported on most modern CPUs and GPUs. In order to use OpenCL in CmdStanR, an OpenCL runtime for the target device must be installed. A guide for the most common devices is available in the -CmdStan manual’s chapter +CmdStan manual’s chapter on parallelization.

In case of using Windows, CmdStan requires the OpenCL.lib to compile the model. If you experience issue @@ -175,17 +177,17 @@

OpenCL.lib file on your system. If you are using CUDA, the path should be similar to the one listed here.

-path_to_opencl_lib <- "C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v11.5/lib/x64"
-cpp_options = list(
-  paste0("LDFLAGS+= -L\"",path_to_opencl_lib,"\" -lOpenCL")
-)
-
-cmdstanr::cmdstan_make_local(cpp_options = cpp_options)
-cmdstanr::rebuild_cmdstan()
+path_to_opencl_lib <- "C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v11.5/lib/x64" +cpp_options = list( + paste0("LDFLAGS+= -L\"",path_to_opencl_lib,"\" -lOpenCL") +) + +cmdstanr::cmdstan_make_local(cpp_options = cpp_options) +cmdstanr::rebuild_cmdstan()

-
-

-Compiling a model with OpenCL

+
+

Compiling a model with OpenCL +

By default, models in CmdStanR are compiled without OpenCL support. Once OpenCL support is enabled, a CmdStan model will make use of OpenCL if the functions in the model support it. Technically no @@ -200,7 +202,7 @@

int<lower=1> k; int<lower=0> n; matrix[n, k] X; - int y[n]; + array[n] int y; } parameters { vector[k] beta; @@ -213,30 +215,30 @@

}

Some fake data will be useful to run this model:

-library(cmdstanr)
-
-# Generate some fake data
-n <- 250000
-k <- 20
-X <- matrix(rnorm(n * k), ncol = k)
-y <- rbinom(n, size = 1, prob = plogis(3 * X[,1] - 2 * X[,2] + 1))
-mdata <- list(k = k, n = n, y = y, X = X)
+library(cmdstanr) + +# Generate some fake data +n <- 250000 +k <- 20 +X <- matrix(rnorm(n * k), ncol = k) +y <- rbinom(n, size = 1, prob = plogis(3 * X[,1] - 2 * X[,2] + 1)) +mdata <- list(k = k, n = n, y = y, X = X)

In this model, most of the computation will be handled by the bernoulli_logit_glm_lpmf function. Because this is a supported GPU function, it should be possible to accelerate it with -OpenCL. Check here for a +OpenCL. Check here for a list of functions with OpenCL support.

To build the model with OpenCL support, add cpp_options = list(stan_opencl = TRUE) at the compilation step.

-# Compile the model with STAN_OPENCL=TRUE
-mod_cl <- cmdstan_model("opencl-files/bernoulli_logit_glm.stan",
-                        cpp_options = list(stan_opencl = TRUE))
+# Compile the model with STAN_OPENCL=TRUE +mod_cl <- cmdstan_model("opencl-files/bernoulli_logit_glm.stan", + cpp_options = list(stan_opencl = TRUE))
-
-

-Running models with OpenCL

+
+

Running models with OpenCL +

Running models with OpenCL requires specifying the OpenCL platform and device on which to run the model (there can be multiple). If the system has one GPU and no OpenCL CPU runtime, the platform and device @@ -245,10 +247,10 @@

devices are available.

On an Ubuntu system with both CPU and GPU OpenCL support, clinfo -l outputs:

-
Platform #0: AMD Accelerated Parallel Processing
- `-- Device #0: gfx906+sram-ecc
-Platform #1: Intel(R) CPU Runtime for OpenCL(TM) Applications
- `-- Device #0: Intel(R) Core(TM) i7-4790 CPU @ 3.60GHz
+
Platform #0: AMD Accelerated Parallel Processing
+ `-- Device #0: gfx906+sram-ecc
+Platform #1: Intel(R) CPU Runtime for OpenCL(TM) Applications
+ `-- Device #0: Intel(R) Core(TM) i7-4790 CPU @ 3.60GHz

On this system the GPU is platform ID 0 and device ID 0, while the CPU is platform ID 1, device ID 0. These can be specified with the opencl_ids argument when running a model. The @@ -256,38 +258,17 @@

first element is the platform ID and the second argument is the device ID.

-fit_cl <- mod_cl$sample(data = mdata, chains = 4, parallel_chains = 4,
-                        opencl_ids = c(0, 0), refresh = 0)
-
Running MCMC with 4 parallel chains...
-
-Chain 4 finished in 96.7 seconds.
-Chain 1 finished in 97.9 seconds.
-Chain 2 finished in 98.6 seconds.
-Chain 3 finished in 98.8 seconds.
-
-All 4 chains finished successfully.
-Mean chain execution time: 98.0 seconds.
-Total execution time: 103.0 seconds.
+fit_cl <- mod_cl$sample(data = mdata, chains = 4, parallel_chains = 4, + opencl_ids = c(0, 0), refresh = 0)

We’ll also run a version without OpenCL and compare the run times.

-
-# no OpenCL version
-mod <- cmdstan_model("opencl-files/bernoulli_logit_glm.stan", force_recompile = TRUE)
-fit_cpu <- mod$sample(data = mdata, chains = 4, parallel_chains = 4, refresh = 0)
-
Running MCMC with 4 parallel chains...
-
-Chain 3 finished in 487.9 seconds.
-Chain 2 finished in 491.8 seconds.
-Chain 1 finished in 514.9 seconds.
-Chain 4 finished in 518.4 seconds.
-
-All 4 chains finished successfully.
-Mean chain execution time: 503.2 seconds.
-Total execution time: 521.9 seconds.
+
+# no OpenCL version
+mod <- cmdstan_model("opencl-files/bernoulli_logit_glm.stan", force_recompile = TRUE)
+fit_cpu <- mod$sample(data = mdata, chains = 4, parallel_chains = 4, refresh = 0)

The speedup of the OpenCL model is:

-
-fit_cpu$time()$total / fit_cl$time()$total
-
[1] 5.065968
+
+fit_cpu$time()$total / fit_cl$time()$total

This speedup will be determined by the particular GPU/CPU used, the input problem sizes (data as well as parameters) and if the model uses functions that can be run on the GPU or other OpenCL devices.

@@ -305,11 +286,13 @@

-

Developed by Jonah Gabry, Rok Češnovar, Andrew Johnson.

+

+

Developed by Jonah Gabry, Rok Češnovar, Andrew Johnson.

-

Site built with pkgdown 1.6.1.

+

+

Site built with pkgdown 2.0.7.

@@ -318,5 +301,7 @@

+ + diff --git a/docs/articles/cmdstanr-internals.html b/docs/articles/cmdstanr-internals.html index 035f2e91..1e55d1e0 100644 --- a/docs/articles/cmdstanr-internals.html +++ b/docs/articles/cmdstanr-internals.html @@ -40,7 +40,7 @@ cmdstanr - 0.6.0 + 0.7.0

@@ -183,10 +183,10 @@

Immediate compilation 
 mod$stan_file()
-
[1] "/Users/jgabry/.cmdstan/cmdstan-2.32.2/examples/bernoulli/bernoulli.stan"
+
[1] "/Users/jgabry/.cmdstan/cmdstan-2.33.1/examples/bernoulli/bernoulli.stan"
 mod$exe_file()
-
[1] "/Users/jgabry/.cmdstan/cmdstan-2.32.2/examples/bernoulli/bernoulli"
+
[1] "/Users/jgabry/.cmdstan/cmdstan-2.33.1/examples/bernoulli/bernoulli"

Subsequently, if you create a CmdStanModel object from the same Stan file then compilation will be skipped (assuming the file hasn’t changed).

@@ -220,7 +220,7 @@

Delayed compilation
 mod$compile()
 mod$exe_file()

-
[1] "/Users/jgabry/.cmdstan/cmdstan-2.32.2/examples/bernoulli/bernoulli"
+
[1] "/Users/jgabry/.cmdstan/cmdstan-2.33.1/examples/bernoulli/bernoulli"

Pedantic check @@ -278,7 +278,7 @@

Pedantic check$compile() method directly if using the delayed compilation approach described above).

mod_pedantic <- cmdstan_model(stan_file_pedantic, pedantic = TRUE)
-Warning in '/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpbI1mWj/model-1134e453cbe1d.stan', line 11, column 14: A
+Warning in '/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/model-131d863015ed4.stan', line 11, column 14: A
     poisson distribution is given parameter lambda as a rate parameter
     (argument 1), but lambda was not constrained to be strictly positive.
 Warning: The parameter lambda has no priors. This means either no prior is
@@ -288,7 +288,7 @@ 
Pedantic checkpedantic argument to the $check_syntax()
 method.

 
mod_pedantic$check_syntax(pedantic = TRUE)
-Warning in '/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpbI1mWj/model_febb1e69c7387a0e64cf13583e078104.stan', line 11, column 14: A
+Warning in '/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/model_febb1e69c7387a0e64cf13583e078104.stan', line 11, column 14: A
     poisson distribution is given parameter lambda as a rate parameter
     (argument 1), but lambda was not constrained to be strictly positive.
 Warning: The parameter lambda has no priors. This means either no prior is
@@ -305,7 +305,7 @@ 
Pedantic check
 mod_pedantic <- cmdstan_model(stan_file_pedantic, compile = FALSE)
 mod_pedantic$check_syntax(pedantic = TRUE)
-Warning in '/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpbI1mWj/model_febb1e69c7387a0e64cf13583e078104.stan', line 11, column 14: A
+Warning in '/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/model_febb1e69c7387a0e64cf13583e078104.stan', line 11, column 14: A
     poisson distribution is given parameter lambda as a rate parameter
     (argument 1), but lambda was not constrained to be strictly positive.
 Warning: The parameter lambda has no priors. This means either no prior is
@@ -495,10 +495,10 @@ 
Default temporary files
 
 fit$output_files()

-
[1] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpbI1mWj/bernoulli-202308170903-1-3cf14f.csv"
-[2] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpbI1mWj/bernoulli-202308170903-2-3cf14f.csv"
-[3] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpbI1mWj/bernoulli-202308170903-3-3cf14f.csv"
-[4] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpbI1mWj/bernoulli-202308170903-4-3cf14f.csv"

+
[1] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/bernoulli-202311081035-1-36f995.csv"
+[2] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/bernoulli-202311081035-2-36f995.csv"
+[3] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/bernoulli-202311081035-3-36f995.csv"
+[4] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/bernoulli-202311081035-4-36f995.csv"

 
These files will be lost if you end your R session or if you remove
 the fit object and force (or wait for) garbage
 collection.

@@ -510,8 +510,8 @@ 
Default temporary filesrm(fit)
 gc()

 
          used (Mb) gc trigger  (Mb) limit (Mb) max used  (Mb)
-Ncells 1275914 68.2    2632161 140.6         NA  2060504 110.1
-Vcells 2251167 17.2    8388608  64.0      32768  3439587  26.3

+Ncells 1249060 66.8    2424964 129.6         NA  2424964 129.6
+Vcells 2192048 16.8    8388608  64.0      32768  3407356  26.0
 file.exists(files)
[1] FALSE FALSE FALSE FALSE
@@ -601,7 +601,7 @@

Lazy CSV reading

After we call a method that requires the draws then if we reexamine @@ -655,7 +655,7 @@

Lazy CSV readingLazy CSV reading

For models with many parameters, transformed parameters, or generated @@ -686,9 +686,9 @@

read_cmdstan_csv()read_cmdstan_csv()read_cmdstan_csv()read_cmdstan_csv()Saving and fit <- mod$sample(data = data_list, save_latent_dynamics = TRUE)

 fit$latent_dynamics_files()
-
[1] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpbI1mWj/bernoulli-diagnostic-202308170903-1-33a5bd.csv"
-[2] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpbI1mWj/bernoulli-diagnostic-202308170903-2-33a5bd.csv"
-[3] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpbI1mWj/bernoulli-diagnostic-202308170903-3-33a5bd.csv"
-[4] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpbI1mWj/bernoulli-diagnostic-202308170903-4-33a5bd.csv"
+
[1] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/bernoulli-diagnostic-202311081035-1-460fca.csv"
+[2] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/bernoulli-diagnostic-202311081035-2-460fca.csv"
+[3] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/bernoulli-diagnostic-202311081035-3-460fca.csv"
+[4] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/bernoulli-diagnostic-202311081035-4-460fca.csv"
 # read one of the files in
 x <- utils::read.csv(fit$latent_dynamics_files()[1], comment.char = "#")
 head(x)
      lp__ accept_stat__ stepsize__ treedepth__ n_leapfrog__ divergent__
-1 -6.81485      0.964189   0.890649           1            3           0
-2 -6.82429      0.975056   0.890649           2            3           0
-3 -6.89143      0.994187   0.890649           2            3           0
-4 -7.74322      0.864876   0.890649           2            7           0
-5 -7.53356      1.000000   0.890649           1            1           0
-6 -7.72985      0.976133   0.890649           1            1           0
-  energy__     theta   p_theta   g_theta
-1  7.72945 -0.859513  1.883460  0.569292
-2  6.99952 -0.843502  0.824417  0.609572
-3  6.89942 -0.751210 -0.176090  0.846694
-4  7.75914 -2.133320 -0.248490 -1.729200
-5  7.78044 -2.006620 -0.978554 -1.577890
-6  7.79615 -2.125570  0.507119 -1.720370
+1 -6.84314 0.979774 0.874237 1 3 0 +2 -7.10483 0.971803 0.874237 2 3 0 +3 -6.85492 1.000000 0.874237 1 1 0 +4 -6.81455 0.951680 0.874237 2 3 0 +5 -6.90994 0.917527 0.874237 2 3 0 +6 -6.75277 1.000000 0.874237 1 3 0 + energy__ theta p_theta g_theta +1 6.84887 -0.814342 0.139388 0.683591 +2 7.10784 -0.558031 -0.101020 1.368040 +3 7.03534 -0.797632 0.782091 0.726389 +4 7.12956 -0.860038 1.033420 0.567976 +5 7.27609 -0.730035 -1.114150 0.902245 +6 6.87890 -1.033970 -0.653912 0.147785

The column lp__ is also provided via fit$draws(), and the columns accept_stat__, stepsize__, treedepth__, @@ -818,13 +818,13 @@

Saving and unique to the latent dynamics file.

 head(x[, c("theta", "p_theta", "g_theta")])
-
      theta   p_theta   g_theta
-1 -0.859513  1.883460  0.569292
-2 -0.843502  0.824417  0.609572
-3 -0.751210 -0.176090  0.846694
-4 -2.133320 -0.248490 -1.729200
-5 -2.006620 -0.978554 -1.577890
-6 -2.125570  0.507119 -1.720370
+
      theta   p_theta  g_theta
+1 -0.814342  0.139388 0.683591
+2 -0.558031 -0.101020 1.368040
+3 -0.797632  0.782091 0.726389
+4 -0.860038  1.033420 0.567976
+5 -0.730035 -1.114150 0.902245
+6 -1.033970 -0.653912 0.147785

Our model has a single parameter theta and the three columns above correspond to theta in the unconstrained space (theta on the constrained @@ -872,16 +872,16 @@

Troubleshooting and debuggingmod <- cmdstan_model(stan_file, force_recompile = TRUE)

Running make \
-  /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpbI1mWj/model-1134e4997b2fe \
+  /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/model-131d8d6680cc \
   "STANCFLAGS +=  --name='bernoulli_model'"
 
 --- Translating Stan model to C++ code ---
-bin/stanc --name='bernoulli_model' --o=/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpbI1mWj/model-1134e4997b2fe.hpp /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpbI1mWj/model-1134e4997b2fe.stan
+bin/stanc --name='bernoulli_model' --o=/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/model-131d8d6680cc.hpp /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/model-131d8d6680cc.stan
 
 --- Compiling, linking C++ code ---
-clang++ -std=c++1y -Wno-unknown-warning-option -Wno-tautological-compare -Wno-sign-compare -D_REENTRANT -Wno-ignored-attributes      -I stan/lib/stan_math/lib/tbb_2020.3/include    -O3 -I src -I stan/src -I stan/lib/rapidjson_1.1.0/ -I lib/CLI11-1.9.1/ -I stan/lib/stan_math/ -I stan/lib/stan_math/lib/eigen_3.4.0 -I stan/lib/stan_math/lib/boost_1.78.0 -I stan/lib/stan_math/lib/sundials_6.1.1/include -I stan/lib/stan_math/lib/sundials_6.1.1/src/sundials    -DBOOST_DISABLE_ASSERTS          -c -include-pch stan/src/stan/model/model_header.hpp.gch -x c++ -o /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpbI1mWj/model-1134e4997b2fe.o /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpbI1mWj/model-1134e4997b2fe.hpp
-clang++ -std=c++1y -Wno-unknown-warning-option -Wno-tautological-compare -Wno-sign-compare -D_REENTRANT -Wno-ignored-attributes      -I stan/lib/stan_math/lib/tbb_2020.3/include    -O3 -I src -I stan/src -I stan/lib/rapidjson_1.1.0/ -I lib/CLI11-1.9.1/ -I stan/lib/stan_math/ -I stan/lib/stan_math/lib/eigen_3.4.0 -I stan/lib/stan_math/lib/boost_1.78.0 -I stan/lib/stan_math/lib/sundials_6.1.1/include -I stan/lib/stan_math/lib/sundials_6.1.1/src/sundials    -DBOOST_DISABLE_ASSERTS               -Wl,-L,"/Users/jgabry/.cmdstan/cmdstan-2.32.2/stan/lib/stan_math/lib/tbb" -Wl,-rpath,"/Users/jgabry/.cmdstan/cmdstan-2.32.2/stan/lib/stan_math/lib/tbb"        /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpbI1mWj/model-1134e4997b2fe.o src/cmdstan/main.o       -Wl,-L,"/Users/jgabry/.cmdstan/cmdstan-2.32.2/stan/lib/stan_math/lib/tbb" -Wl,-rpath,"/Users/jgabry/.cmdstan/cmdstan-2.32.2/stan/lib/stan_math/lib/tbb"     stan/lib/stan_math/lib/sundials_6.1.1/lib/libsundials_nvecserial.a stan/lib/stan_math/lib/sundials_6.1.1/lib/libsundials_cvodes.a stan/lib/stan_math/lib/sundials_6.1.1/lib/libsundials_idas.a stan/lib/stan_math/lib/sundials_6.1.1/lib/libsundials_kinsol.a  stan/lib/stan_math/lib/tbb/libtbb.dylib stan/lib/stan_math/lib/tbb/libtbbmalloc.dylib stan/lib/stan_math/lib/tbb/libtbbmalloc_proxy.dylib -o /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpbI1mWj/model-1134e4997b2fe
-rm -f /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpbI1mWj/model-1134e4997b2fe.o
+clang++ -Wno-deprecated-declarations -Wno-deprecated-declarations -std=c++1y -Wno-unknown-warning-option -Wno-tautological-compare -Wno-sign-compare -D_REENTRANT -Wno-ignored-attributes -I stan/lib/stan_math/lib/tbb_2020.3/include -O3 -I src -I stan/src -I stan/lib/rapidjson_1.1.0/ -I lib/CLI11-1.9.1/ -I stan/lib/stan_math/ -I stan/lib/stan_math/lib/eigen_3.4.0 -I stan/lib/stan_math/lib/boost_1.78.0 -I stan/lib/stan_math/lib/sundials_6.1.1/include -I stan/lib/stan_math/lib/sundials_6.1.1/src/sundials -DBOOST_DISABLE_ASSERTS -c -include-pch stan/src/stan/model/model_header_13_0.hpp.gch -x c++ -o /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/model-131d8d6680cc.o /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/model-131d8d6680cc.hpp +clang++ -Wno-deprecated-declarations -Wno-deprecated-declarations -std=c++1y -Wno-unknown-warning-option -Wno-tautological-compare -Wno-sign-compare -D_REENTRANT -Wno-ignored-attributes -I stan/lib/stan_math/lib/tbb_2020.3/include -O3 -I src -I stan/src -I stan/lib/rapidjson_1.1.0/ -I lib/CLI11-1.9.1/ -I stan/lib/stan_math/ -I stan/lib/stan_math/lib/eigen_3.4.0 -I stan/lib/stan_math/lib/boost_1.78.0 -I stan/lib/stan_math/lib/sundials_6.1.1/include -I stan/lib/stan_math/lib/sundials_6.1.1/src/sundials -DBOOST_DISABLE_ASSERTS -Wl,-L,"/Users/jgabry/.cmdstan/cmdstan-2.33.1/stan/lib/stan_math/lib/tbb" -Wl,-rpath,"/Users/jgabry/.cmdstan/cmdstan-2.33.1/stan/lib/stan_math/lib/tbb" /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/model-131d8d6680cc.o src/cmdstan/main.o -Wl,-L,"/Users/jgabry/.cmdstan/cmdstan-2.33.1/stan/lib/stan_math/lib/tbb" -Wl,-rpath,"/Users/jgabry/.cmdstan/cmdstan-2.33.1/stan/lib/stan_math/lib/tbb" stan/lib/stan_math/lib/sundials_6.1.1/lib/libsundials_nvecserial.a stan/lib/stan_math/lib/sundials_6.1.1/lib/libsundials_cvodes.a stan/lib/stan_math/lib/sundials_6.1.1/lib/libsundials_idas.a stan/lib/stan_math/lib/sundials_6.1.1/lib/libsundials_kinsol.a stan/lib/stan_math/lib/tbb/libtbb.dylib stan/lib/stan_math/lib/tbb/libtbbmalloc.dylib stan/lib/stan_math/lib/tbb/libtbbmalloc_proxy.dylib -o /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/model-131d8d6680cc +rm -f /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/model-131d8d6680cc.o 
 fit <- mod$sample(
   data = data_list,
@@ -891,11 +891,11 @@ 
Troubleshooting and debugging)
Running MCMC with 1 chain...
 
-Running ./bernoulli 'id=1' random 'seed=2144173564' data \
-  'file=/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpbI1mWj/standata-1134e60662723.json' \
+Running ./bernoulli 'id=1' random 'seed=2046085541' data \
+  'file=/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/standata-131d874013405.json' \
   output \
-  'file=/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpbI1mWj/bernoulli-202308170903-1-93b959.csv' \
-  'profile_file=/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpbI1mWj/bernoulli-profile-202308170903-1-09cf37.csv' \
+  'file=/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/bernoulli-202311081036-1-2ef601.csv' \
+  'profile_file=/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpT1Jbvd/bernoulli-profile-202311081036-1-0c4891.csv' \
   'method=sample' 'num_samples=100' 'num_warmup=100' 'save_warmup=0' \
   'algorithm=hmc' 'engine=nuts' adapt 'engaged=1'
 Chain 1 method = sample (Default) 
@@ -925,19 +925,19 @@ 
Troubleshooting and debugging
-
+    
 
     

       

 
@@ -49,7 +49,7 @@
 
  • 
   
     
-
+     
   
 
    • 
 
  • 
@@ -64,7 +64,7 @@
 
  • 
   
   
@@ -125,7 +125,7 @@
 
    • 
 
 
-
+      
 
       
    
   
    
@@ -133,15 +133,15 @@
       
    Getting started with CmdStanR
    
                         
    Jonah Gabry and
 Rok Češnovar
    
-
-
+            
+      
       Source: vignettes/cmdstanr.Rmd
       
 
     
    
 
-
-
+    
+    
 
    
 
    Introduction
 
    
@@ -214,10 +214,10 @@ 
    Installing CmdStancmdstan_version():
    
 
-
    [1] "/Users/jgabry/.cmdstan/cmdstan-2.32.2"
    
+
    [1] "/Users/jgabry/.cmdstan/cmdstan-2.33.1"
    
 
-
    [1] "2.32.2"
    
+
    [1] "2.33.1"
    
 
    
 
    
 
    Compiling a model
@@ -254,7 +254,7 @@ 
    Compiling a model
     mod$exe_file()
    
-
    [1] "/Users/jgabry/.cmdstan/cmdstan-2.32.2/examples/bernoulli/bernoulli"
    
+
    [1] "/Users/jgabry/.cmdstan/cmdstan-2.33.1/examples/bernoulli/bernoulli"
    
 
    
 
    
 
    Running MCMC
@@ -277,30 +277,30 @@ 
    Running MCMC)
    Running MCMC with 4 parallel chains...
 
-Chain 1 Iteration:    1 / 2000 [  0%]  (Warmup)
-Chain 1 Iteration:  500 / 2000 [ 25%]  (Warmup)
-Chain 1 Iteration: 1000 / 2000 [ 50%]  (Warmup)
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-Chain 4 Iteration: 1001 / 2000 [ 50%]  (Sampling)
-Chain 4 Iteration: 1500 / 2000 [ 75%]  (Sampling)
-Chain 4 Iteration: 2000 / 2000 [100%]  (Sampling)
+Chain 1 Iteration:    1 / 2000 [  0%]  (Warmup) 
+Chain 1 Iteration:  500 / 2000 [ 25%]  (Warmup) 
+Chain 1 Iteration: 1000 / 2000 [ 50%]  (Warmup) 
+Chain 1 Iteration: 1001 / 2000 [ 50%]  (Sampling) 
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+Chain 2 Iteration:    1 / 2000 [  0%]  (Warmup) 
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+Chain 2 Iteration: 1000 / 2000 [ 50%]  (Warmup) 
+Chain 2 Iteration: 1001 / 2000 [ 50%]  (Sampling) 
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+Chain 3 Iteration:    1 / 2000 [  0%]  (Warmup) 
+Chain 3 Iteration:  500 / 2000 [ 25%]  (Warmup) 
+Chain 3 Iteration: 1000 / 2000 [ 50%]  (Warmup) 
+Chain 3 Iteration: 1001 / 2000 [ 50%]  (Sampling) 
+Chain 3 Iteration: 1500 / 2000 [ 75%]  (Sampling) 
+Chain 3 Iteration: 2000 / 2000 [100%]  (Sampling) 
+Chain 4 Iteration:    1 / 2000 [  0%]  (Warmup) 
+Chain 4 Iteration:  500 / 2000 [ 25%]  (Warmup) 
+Chain 4 Iteration: 1000 / 2000 [ 50%]  (Warmup) 
+Chain 4 Iteration: 1001 / 2000 [ 50%]  (Sampling) 
+Chain 4 Iteration: 1500 / 2000 [ 75%]  (Sampling) 
+Chain 4 Iteration: 2000 / 2000 [100%]  (Sampling) 
 Chain 1 finished in 0.0 seconds.
 Chain 2 finished in 0.0 seconds.
 Chain 3 finished in 0.0 seconds.
@@ -308,7 +308,7 @@ 
    Running MCMCSampler diagnostic warnings a
 suffers from divergences.
    
     
-
    Warning: 67 of 4000 (2.0%) transitions ended with a divergence.
+
    Chain 3 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
    
+
    Chain 3 Exception: normal_lpdf: Scale parameter is 0, but must be positive! (in '/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/Rtmpo8OtTr/model-138066584791a.stan', line 14, column 2 to column 41)
    
+
    Chain 3 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
    
+
    Chain 3 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
    
+
    Chain 3 
    
+
    Warning: 123 of 4000 (3.0%) transitions ended with a divergence.
 See https://mc-stan.org/misc/warnings for details.
    
 
    After fitting there is a warning about divergences. We can also
 regenerate this warning message later using
 fit$diagnostic_summary().
    
-
    +
     diagnostics <- fit_with_warning$diagnostic_summary()
    
-
    Warning: 67 of 4000 (2.0%) transitions ended with a divergence.
+
    Warning: 123 of 4000 (3.0%) transitions ended with a divergence.
 See https://mc-stan.org/misc/warnings for details.
    
-
    +
     print(diagnostics)
    
 
    $num_divergent
-[1]  6 35 24  2
+[1] 24 21 36 42
 
 $num_max_treedepth
 [1] 0 0 0 0
 
 $ebfmi
-[1] 0.30 0.33 0.24 0.35
    
-
    +[1] 0.25 0.44 0.22 0.35
    
+
     # number of divergences reported in warning is the sum of the per chain values
 sum(diagnostics$num_divergent)
    
-
    [1] 67
    
+
    [1] 123
    
 
    
 
    
 
    CmdStan’s diagnose utility
@@ -539,7 +544,7 @@ 
    Create a stanfit object
    +
 
    
 
    
@@ -548,37 +553,96 @@ 
    Running optimization and
 
    
 
    CmdStanR also supports running Stan’s optimization algorithms and its
 algorithms for variational approximation of full Bayesian inference.
-These are run via the $optimize() and
-$variational() methods, which are called in a similar way
-to the $sample() method demonstrated above.
    
+These are run via the $optimize(), $laplace(),
+and $variational() methods, which are called in a similar
+way to the $sample() method demonstrated above.
    
 
    
 
    Optimization
 
    
 
    We can find the (penalized) maximum likelihood estimate (MLE) using
 $optimize().
    
-
    +
     fit_mle <- mod$optimize(data = data_list, seed = 123)
    
-
    Initial log joint probability = -9.51104
-    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes
-       6      -5.00402   0.000103557   2.55661e-07           1           1        9
-Optimization terminated normally:
-  Convergence detected: relative gradient magnitude is below tolerance
-Finished in  0.2 seconds.
    
-
    +
    Initial log joint probability = -9.51104 
+    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes  
+       6      -5.00402   0.000103557   2.55661e-07           1           1        9    
+Optimization terminated normally:  
+  Convergence detected: relative gradient magnitude is below tolerance 
+Finished in  0.1 seconds.
    
+
     fit_mle$print() # includes lp__ (log prob calculated by Stan program)
    
 
     variable estimate
     lp__     -5.00
     theta     0.20
    
-
    +
     fit_mle$mle("theta")
    
 
    theta 
   0.2 
    
 
    Here’s a plot comparing the penalized MLE to the posterior
 distribution of theta.
    
-
    +
     mcmc_hist(fit$draws("theta")) +
   vline_at(fit_mle$mle("theta"), size = 1.5)
    
 
    
+
    For optimization, by default the mode is calculated without the
+Jacobian adjustment for constrained variables, which shifts the mode due
+to the change of variables. To include the Jacobian adjustment and
+obtain a maximum a posteriori (MAP) estimate set
+jacobian=TRUE. See the Maximum
+Likelihood Estimation section of the CmdStan User’s Guide for more
+details.
    
+
    +fit_map <- mod$optimize(
+  data = data_list,
+  jacobian = TRUE,
+  seed = 123
+)
    
+
    Initial log joint probability = -11.0088 
+    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes  
+       5      -6.74802   0.000938344   1.39149e-05           1           1        8    
+Optimization terminated normally:  
+  Convergence detected: relative gradient magnitude is below tolerance 
+Finished in  0.1 seconds.
    
+
    
+
    
+
    Laplace Approximation
+
    
+
    The $laplace()
+produces a sample from a normal approximation centered at the mode of a
+distribution in the unconstrained space. If the mode is a MAP estimate,
+the samples provide an estimate of the mean and standard deviation of
+the posterior distribution. If the mode is the MLE, the sample provides
+an estimate of the standard error of the likelihood. Whether the mode is
+the MAP or MLE depends on the value of the jacobian
+argument when running optimization. See the Laplace
+Sampling chapter of the CmdStan User’s Guide for more details.
    
+
    Here we pass in the fit_map object from above as the
+mode argument. If mode is omitted then
+optimization will be run internally before taking draws from the normal
+approximation.
    
+
    +fit_laplace <- mod$laplace(
+    mode = fit_map,
+    draws = 4000,
+    data = data_list,
+    seed = 123,
+    refresh = 1000
+  )
    
+
    Calculating Hessian 
+Calculating inverse of Cholesky factor 
+Generating draws 
+iteration: 0 
+iteration: 1000 
+iteration: 2000 
+iteration: 3000 
+Finished in  0.1 seconds.
    
+
    +fit_laplace$print("theta")
    
+
     variable mean median   sd  mad   q5  q95
+    theta 0.27   0.25 0.12 0.12 0.10 0.50
    
+
    +mcmc_hist(fit_laplace$draws("theta"), binwidth = 0.025)
    
+
    
 
    
 
    
 
    Variational Bayes
@@ -586,59 +650,64 @@ 
    Variational Bayes$variational()
 method.
    
-
    -fit_vb <- mod$variational(data = data_list, seed = 123, output_samples = 4000)
    
-
    ------------------------------------------------------------
-EXPERIMENTAL ALGORITHM:
-  This procedure has not been thoroughly tested and may be unstable
-  or buggy. The interface is subject to change.
-------------------------------------------------------------
-Gradient evaluation took 6e-06 seconds
-1000 transitions using 10 leapfrog steps per transition would take 0.06 seconds.
-Adjust your expectations accordingly!
-Begin eta adaptation.
-Iteration:   1 / 250 [  0%]  (Adaptation)
-Iteration:  50 / 250 [ 20%]  (Adaptation)
-Iteration: 100 / 250 [ 40%]  (Adaptation)
-Iteration: 150 / 250 [ 60%]  (Adaptation)
-Iteration: 200 / 250 [ 80%]  (Adaptation)
-Success! Found best value [eta = 1] earlier than expected.
-Begin stochastic gradient ascent.
-  iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes
-   100           -6.262             1.000            1.000
-   200           -6.263             0.500            1.000
-   300           -6.307             0.336            0.007   MEDIAN ELBO CONVERGED
-Drawing a sample of size 4000 from the approximate posterior...
-COMPLETED.
+
    +fit_vb <- mod$variational(
+  data = data_list,
+  seed = 123,
+  draws = 4000
+)
    
+
    ------------------------------------------------------------ 
+EXPERIMENTAL ALGORITHM: 
+  This procedure has not been thoroughly tested and may be unstable 
+  or buggy. The interface is subject to change. 
+------------------------------------------------------------ 
+Gradient evaluation took 5e-06 seconds 
+1000 transitions using 10 leapfrog steps per transition would take 0.05 seconds. 
+Adjust your expectations accordingly! 
+Begin eta adaptation. 
+Iteration:   1 / 250 [  0%]  (Adaptation) 
+Iteration:  50 / 250 [ 20%]  (Adaptation) 
+Iteration: 100 / 250 [ 40%]  (Adaptation) 
+Iteration: 150 / 250 [ 60%]  (Adaptation) 
+Iteration: 200 / 250 [ 80%]  (Adaptation) 
+Success! Found best value [eta = 1] earlier than expected. 
+Begin stochastic gradient ascent. 
+  iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes  
+   100           -6.262             1.000            1.000 
+   200           -6.263             0.500            1.000 
+   300           -6.307             0.336            0.007   MEDIAN ELBO CONVERGED 
+Drawing a sample of size 4000 from the approximate posterior...  
+COMPLETED. 
 Finished in  0.1 seconds.
    
-
    +
     fit_vb$print("theta")
    
 
     variable mean median   sd  mad   q5  q95
     theta 0.27   0.25 0.12 0.12 0.10 0.49
    
-
    The $draws() method can be used to access the
-approximate posterior draws. Let’s extract the draws, make the same plot
-we made after MCMC, and compare the two. In this trivial example the
-distributions look quite similar, although the variational approximation
-slightly underestimates the posterior standard deviation.
    
-
    -mcmc_hist(fit$draws("theta"), binwidth = 0.025)
    
-
    
-Posterior from MCMC
    
-Posterior from MCMC
-
    
-
    
-
    -mcmc_hist(fit_vb$draws("theta"), binwidth = 0.025)
    
-
    
-Posterior from variational
    
-Posterior from variational
-
    
-
    
-
    For more details on the $optimize() and
-$variational() methods, follow these links to their
-documentation pages.
    
+
    Let’s extract the draws, make the same plot we made after MCMC and
+Laplace approximation, and compare them all. In this simple example the
+distributions are quite similar, but this will not always be the
+case.
    
+
    +mcmc_hist(fit_vb$draws("theta"), binwidth = 0.025) +
+  ggplot2::labs(subtitle = "Approximate posterior from variational") +
+  ggplot2::xlim(0, 1)
    
+
    
+
    +mcmc_hist(fit_laplace$draws("theta"), binwidth = 0.025) +
+  ggplot2::labs(subtitle = "Approximate posterior from Laplace") +
+  ggplot2::xlim(0, 1)
    
+
    
+
    +mcmc_hist(fit$draws("theta"), binwidth = 0.025) +
+  ggplot2::labs(subtitle = "Posterior from MCMC") +
+  ggplot2::xlim(0, 1)
    
+
    
+
    For more details on the $optimize(),
+$laplace() and $variational() methods, follow
+these links to their documentation pages.
    
 
 
    
@@ -650,7 +719,7 @@ 
    Saving fitted model objects
    +
     fit$save_object(file = "fit.RDS")
 
 # can be read back in using readRDS
@@ -662,7 +731,7 @@ 
    Saving fitted model objects$save_object()
 and replace saveRDS with the much faster
 qsave() function from the qs package.
    
-
    +
     # Load CmdStan output files into the fitted model object.
 fit$draws() # Load posterior draws into the object.
 try(fit$sampler_diagnostics(), silent = TRUE) # Load sampler diagnostics.
@@ -678,7 +747,7 @@ 
    Saving fitted model objects
    +
 
@@ -118,8 +118,6 @@ 
    More details
    
         
    
         
    R Markdown CmdStan Engine
    
         
    
-        
    Handling deprecated Stan features with the canonicalizer in CmdStanR
    
-        
    
         
    Profiling Stan programs with CmdStanR
    
         
    
         
    Running Stan on the GPU with OpenCL
    
diff --git a/docs/articles/posterior.html b/docs/articles/posterior.html
index 24f6b6a3..1d5cc3e4 100644
--- a/docs/articles/posterior.html
+++ b/docs/articles/posterior.html
@@ -40,7 +40,7 @@
       
       
         cmdstanr
-        0.6.0
+        0.7.0
       
     
    
 
@@ -140,78 +140,80 @@ 
    Working with Posteriors
    
 
     
     
-
 
    
 
    Summary statistics
 
    
-
    We can easily customise the summary statistics reported by
+
    We can easily customize the summary statistics reported by
 $summary() and $print().
    
 
    -fit <- cmdstanr::cmdstanr_example("schools", method = "sample")
-fit$summary()
    
-
    Warning: 302 of 4000 (8.0%) transitions ended with a divergence.
+fit <- cmdstanr::cmdstanr_example("schools", method = "sample")
    
+
    Warning: 129 of 4000 (3.0%) transitions ended with a divergence.
 See https://mc-stan.org/misc/warnings for details.
    
-
    Warning: 1 of 4 chains had an E-BFMI less than 0.2.
-See https://mc-stan.org/misc/warnings for details.
    
-
       variable  mean median  sd mad      q5 q95 rhat ess_bulk ess_tail
-1      lp__ -56.7  -57.1 6.1 6.4 -66.008 -46  1.1       33       19
-2        mu   6.6    6.7 4.2 4.5   0.025  13  1.0      124      879
-3       tau   4.7    3.9 3.6 3.4   0.790  12  1.1       33       22
-4  theta[1]   9.1    8.5 6.8 6.0  -0.082  21  1.0      163      405
-5  theta[2]   7.0    6.9 5.5 5.8  -1.456  16  1.0      236     1810
-6  theta[3]   5.7    6.1 6.4 6.2  -4.835  16  1.0      313     1466
-7  theta[4]   6.8    6.9 5.9 5.8  -2.355  16  1.0      246     1356
-8  theta[5]   4.9    5.2 5.8 5.5  -5.038  13  1.0      219      932
-9  theta[6]   5.7    5.9 5.8 5.8  -4.073  15  1.0      278     1208
-10 theta[7]   8.9    8.6 6.0 5.7   0.094  19  1.0      176      352
-11 theta[8]   7.0    7.1 6.5 5.9  -3.088  18  1.0      317     1709
    
+
    +fit$summary()
    
+
    
[38;5;246m# A tibble: 11 × 10
[39m
+   variable   mean median    sd   mad      q5   q95  rhat ess_bulk ess_tail
+   
[3m
[38;5;246m<chr>
[39m
[23m     
[3m
[38;5;246m<dbl>
[39m
[23m  
[3m
[38;5;246m<dbl>
[39m
[23m 
[3m
[38;5;246m<dbl>
[39m
[23m 
[3m
[38;5;246m<dbl>
[39m
[23m   
[3m
[38;5;246m<dbl>
[39m
[23m 
[3m
[38;5;246m<dbl>
[39m
[23m 
[3m
[38;5;246m<dbl>
[39m
[23m    
[3m
[38;5;246m<dbl>
[39m
[23m    
[3m
[38;5;246m<dbl>
[39m
[23m
+
[38;5;250m 1
[39m lp__     -
[31m57
[39m
[31m.
[39m
[31m8
[39m  -
[31m58
[39m
[31m.
[39m
[31m2
[39m   5.49  5.70 -
[31m66
[39m
[31m.
[39m
[31m4
[39m   -
[31m48
[39m
[31m.
[39m
[31m3
[39m  1.02     167.     73.4
+
[38;5;250m 2
[39m mu         6.41   6.52  4.26  3.98  -
[31m0
[39m
[31m.
[39m
[31m818
[39m  13.3  1.00     879.   
[4m1
[24m035. 
+
[38;5;250m 3
[39m tau        5.25   4.42  3.65  3.35   1.05   12.3  1.02     165.     74.2
+
[38;5;250m 4
[39m theta[1]   9.31   8.49  7.20  5.99  -
[31m0
[39m
[31m.
[39m
[31m848
[39m  22.7  1.00    
[4m1
[24m019.   
[4m1
[24m427. 
+
[38;5;250m 5
[39m theta[2]   6.79   6.59  5.67  5.19  -
[31m2
[39m
[31m.
[39m
[31m48
[39m   16.0  1.00    
[4m1
[24m144.   
[4m1
[24m560. 
+
[38;5;250m 6
[39m theta[3]   5.26   5.83  6.76  5.56  -
[31m6
[39m
[31m.
[39m
[31m39
[39m   15.5  1.00    
[4m1
[24m331.   
[4m1
[24m585. 
+
[38;5;250m 7
[39m theta[4]   6.50   6.53  5.87  5.41  -
[31m3
[39m
[31m.
[39m
[31m19
[39m   16.4  1.00    
[4m1
[24m180.   
[4m1
[24m550. 
+
[38;5;250m 8
[39m theta[5]   4.65   5.06  5.63  5.08  -
[31m5
[39m
[31m.
[39m
[31m11
[39m   13.6  1.01    
[4m1
[24m173.   
[4m1
[24m709. 
+
[38;5;250m 9
[39m theta[6]   5.45   5.88  5.82  5.38  -
[31m4
[39m
[31m.
[39m
[31m57
[39m   14.4  1.00    
[4m1
[24m435.   
[4m1
[24m879. 
+
[38;5;250m10
[39m theta[7]   9.10   8.57  6.15  5.73  -
[31m0
[39m
[31m.
[39m
[31m198
[39m  19.7  1.00     857.    987. 
+
[38;5;250m11
[39m theta[8]   7.07   6.89  6.47  5.57  -
[31m3
[39m
[31m.
[39m
[31m26
[39m   17.8  1.01    
[4m1
[24m358.   
[4m1
[24m753. 
    
 
    By default all variables are summaries with the follow functions:
    
 
 
    [1] "mean"      "median"    "sd"        "mad"       "quantile2"
    
-
    To change the variables summarised, we use the variables argument
    
+
    To change the variables summarized, we use the variables argument
    
 
     fit$summary(variables = c("mu", "tau"))
    
-
      variable mean median  sd mad    q5 q95 rhat ess_bulk ess_tail
-1       mu  6.6    6.7 4.2 4.5 0.025  13  1.0      124      879
-2      tau  4.7    3.9 3.6 3.4 0.790  12  1.1       33       22
    
+
    
[38;5;246m# A tibble: 2 × 10
[39m
+  variable  mean median    sd   mad     q5   q95  rhat ess_bulk ess_tail
+  
[3m
[38;5;246m<chr>
[39m
[23m    
[3m
[38;5;246m<dbl>
[39m
[23m  
[3m
[38;5;246m<dbl>
[39m
[23m 
[3m
[38;5;246m<dbl>
[39m
[23m 
[3m
[38;5;246m<dbl>
[39m
[23m  
[3m
[38;5;246m<dbl>
[39m
[23m 
[3m
[38;5;246m<dbl>
[39m
[23m 
[3m
[38;5;246m<dbl>
[39m
[23m    
[3m
[38;5;246m<dbl>
[39m
[23m    
[3m
[38;5;246m<dbl>
[39m
[23m
+
[38;5;250m1
[39m mu        6.41   6.52  4.26  3.98 -
[31m0
[39m
[31m.
[39m
[31m818
[39m  13.3  1.00     879.   
[4m1
[24m035. 
+
[38;5;250m2
[39m tau       5.25   4.42  3.65  3.35  1.05   12.3  1.02     165.     74.2
    
 
    We can additionally change which functions are used
    
 
     fit$summary(variables = c("mu", "tau"), mean, sd)
    
-
      variable mean  sd
-1       mu  6.6 4.2
-2      tau  4.7 3.6
    
-
    To summarise all variables with non-default functions, it is
+
    
[38;5;246m# A tibble: 2 × 3
[39m
+  variable  mean    sd
+  
[3m
[38;5;246m<chr>
[39m
[23m    
[3m
[38;5;246m<dbl>
[39m
[23m 
[3m
[38;5;246m<dbl>
[39m
[23m
+
[38;5;250m1
[39m mu        6.41  4.26
+
[38;5;250m2
[39m tau       5.25  3.65
    
+
    To summarize all variables with non-default functions, it is
 necessary to set explicitly set the variables argument, either to
 NULL or the full vector of variable names.
    
 
    -fit$metadata()$model_params
-fit$summary(variables = NULL, "mean", "median")
    
+fit$metadata()$model_params
    
 
     [1] "lp__"     "mu"       "tau"      "theta[1]" "theta[2]" "theta[3]"
  [7] "theta[4]" "theta[5]" "theta[6]" "theta[7]" "theta[8]"
    
-
       variable  mean median
-1      lp__ -56.7  -57.1
-2        mu   6.6    6.7
-3       tau   4.7    3.9
-4  theta[1]   9.1    8.5
-5  theta[2]   7.0    6.9
-6  theta[3]   5.7    6.1
-7  theta[4]   6.8    6.9
-8  theta[5]   4.9    5.2
-9  theta[6]   5.7    5.9
-10 theta[7]   8.9    8.6
-11 theta[8]   7.0    7.1
    
+
    +fit$summary(variables = NULL, "mean", "median")
    
+
    
[38;5;246m# A tibble: 11 × 3
[39m
+   variable   mean median
+   
[3m
[38;5;246m<chr>
[39m
[23m     
[3m
[38;5;246m<dbl>
[39m
[23m  
[3m
[38;5;246m<dbl>
[39m
[23m
+
[38;5;250m 1
[39m lp__     -
[31m57
[39m
[31m.
[39m
[31m8
[39m  -
[31m58
[39m
[31m.
[39m
[31m2
[39m 
+
[38;5;250m 2
[39m mu         6.41   6.52
+
[38;5;250m 3
[39m tau        5.25   4.42
+
[38;5;250m 4
[39m theta[1]   9.31   8.49
+
[38;5;250m 5
[39m theta[2]   6.79   6.59
+
[38;5;250m 6
[39m theta[3]   5.26   5.83
+
[38;5;250m 7
[39m theta[4]   6.50   6.53
+
[38;5;250m 8
[39m theta[5]   4.65   5.06
+
[38;5;250m 9
[39m theta[6]   5.45   5.88
+
[38;5;250m10
[39m theta[7]   9.10   8.57
+
[38;5;250m11
[39m theta[8]   7.07   6.89
    
 
    Summary functions can be specified by character string, function, or
 using a formula (or anything else supported by [rlang::as_function]). If
 these arguments are named, those names will be used in the tibble
 output. If the summary results are named they will take precedence.
    
-
    -my_sd <- function(x) c(My_SD = sd(x))
+
    +my_sd <- function(x) c(My_SD = sd(x))
 fit$summary(
   c("mu", "tau"), 
   MEAN = mean, 
@@ -220,60 +222,71 @@ 
    Summary statistics~quantile(.x, probs = c(0.1, 0.9)),
   Minimum = function(x) min(x)
 )        
    
-
      variable MEAN median My_SD 10%  90% Minimum
-1       mu  6.6    6.7   4.2 1.3 11.7  -11.23
-2      tau  4.7    3.9   3.6 1.1  9.6    0.53
    
+
    
[38;5;246m# A tibble: 2 × 7
[39m
+  variable  MEAN median My_SD `10%` `90%` Minimum
+  
[3m
[38;5;246m<chr>
[39m
[23m    
[3m
[38;5;246m<dbl>
[39m
[23m  
[3m
[38;5;246m<dbl>
[39m
[23m 
[3m
[38;5;246m<dbl>
[39m
[23m 
[3m
[38;5;246m<dbl>
[39m
[23m 
[3m
[38;5;246m<dbl>
[39m
[23m   
[3m
[38;5;246m<dbl>
[39m
[23m
+
[38;5;250m1
[39m mu        6.41   6.52  4.26 0.940  11.5 -
[31m10
[39m
[31m.
[39m
[31m7
[39m  
+
[38;5;250m2
[39m tau       5.25   4.42  3.65 1.47   10.3   0.516
    
 
    Arguments to all summary functions can also be specified with
 .args.
    
-
    +
     fit$summary(c("mu", "tau"), quantile, .args = list(probs = c(0.025, .05, .95, .975)))
    
-
      variable  2.5%    5% 95% 97.5%
-1       mu -1.17 0.025  13    15
-2      tau  0.59 0.790  12    13
    
+
    
[38;5;246m# A tibble: 2 × 5
[39m
+  variable `2.5%`   `5%` `95%` `97.5%`
+  
[3m
[38;5;246m<chr>
[39m
[23m     
[3m
[38;5;246m<dbl>
[39m
[23m  
[3m
[38;5;246m<dbl>
[39m
[23m 
[3m
[38;5;246m<dbl>
[39m
[23m   
[3m
[38;5;246m<dbl>
[39m
[23m
+
[38;5;250m1
[39m mu       -
[31m2
[39m
[31m.
[39m
[31m32
[39m  -
[31m0
[39m
[31m.
[39m
[31m818
[39m  13.3    14.5
+
[38;5;250m2
[39m tau       0.760  1.05   12.3    14.2
    
 
    The summary functions are applied to the array of sample values, with
 dimension iter_samplingxchains.
    
-
    +
     fit$summary(variables = NULL, dim, colMeans)
    
-
       variable dim.1 dim.2     1     2     3     4
-1      lp__  1000     4 -58.0 -55.7 -55.7 -57.3
-2        mu  1000     4   6.9   7.5   5.4   6.8
-3       tau  1000     4   5.2   4.3   4.4   4.9
-4  theta[1]  1000     4  10.0   9.7   7.6   9.1
-5  theta[2]  1000     4   7.1   8.0   5.8   7.2
-6  theta[3]  1000     4   5.7   6.6   4.5   5.9
-7  theta[4]  1000     4   7.2   7.7   5.6   6.7
-8  theta[5]  1000     4   4.9   6.0   4.0   4.9
-9  theta[6]  1000     4   5.7   6.7   4.8   5.7
-10 theta[7]  1000     4   9.3   9.5   7.5   9.2
-11 theta[8]  1000     4   7.0   8.0   5.9   7.0
    
+
    
[38;5;246m# A tibble: 11 × 7
[39m
+   variable dim.1 dim.2    `1`    `2`    `3`    `4`
+   
[3m
[38;5;246m<chr>
[39m
[23m    
[3m
[38;5;246m<dbl>
[39m
[23m 
[3m
[38;5;246m<dbl>
[39m
[23m  
[3m
[38;5;246m<dbl>
[39m
[23m  
[3m
[38;5;246m<dbl>
[39m
[23m  
[3m
[38;5;246m<dbl>
[39m
[23m  
[3m
[38;5;246m<dbl>
[39m
[23m
+
[38;5;250m 1
[39m lp__      
[4m1
[24m000     4 -
[31m57
[39m
[31m.
[39m
[31m8
[39m  -
[31m57
[39m
[31m.
[39m
[31m9
[39m  -
[31m59
[39m
[31m.
[39m
[31m0
[39m  -
[31m56
[39m
[31m.
[39m
[31m7
[39m 
+
[38;5;250m 2
[39m mu        
[4m1
[24m000     4   6.61   6.42   6.48   6.11
+
[38;5;250m 3
[39m tau       
[4m1
[24m000     4   4.94   5.36   5.89   4.83
+
[38;5;250m 4
[39m theta[1]  
[4m1
[24m000     4   9.42   9.27   9.92   8.65
+
[38;5;250m 5
[39m theta[2]  
[4m1
[24m000     4   7.07   6.65   6.95   6.47
+
[38;5;250m 6
[39m theta[3]  
[4m1
[24m000     4   5.54   5.17   5.47   4.86
+
[38;5;250m 7
[39m theta[4]  
[4m1
[24m000     4   6.57   6.50   6.67   6.28
+
[38;5;250m 8
[39m theta[5]  
[4m1
[24m000     4   4.87   4.55   4.50   4.67
+
[38;5;250m 9
[39m theta[6]  
[4m1
[24m000     4   5.64   5.56   5.43   5.18
+
[38;5;250m10
[39m theta[7]  
[4m1
[24m000     4   8.97   9.29   9.63   8.50
+
[38;5;250m11
[39m theta[8]  
[4m1
[24m000     4   7.00   7.16   7.37   6.73
    
 
    For this reason users may have unexpected results if they use
 stats::var() directly, as it will return a covariance
 matrix. An alternative is the distributional::variance()
 function, which can also be accessed via
 posterior::variance().
    
-
    -fit$summary(c("mu", "tau"), posterior::variance, ~var(as.vector(.x)))
    
-
      variable posterior::variance ~var(as.vector(.x))
-1       mu                  18                  18
-2      tau                  13                  13
    
+
    +fit$summary(c("mu", "tau"), posterior::variance, ~var(as.vector(.x)))
    
+
    
[38;5;246m# A tibble: 2 × 3
[39m
+  variable `posterior::variance` `~var(as.vector(.x))`
+  
[3m
[38;5;246m<chr>
[39m
[23m                    
[3m
[38;5;246m<dbl>
[39m
[23m                 
[3m
[38;5;246m<dbl>
[39m
[23m
+
[38;5;250m1
[39m mu                        18.2                  18.2
+
[38;5;250m2
[39m tau                       13.3                  13.3
    
 
    Summary functions need not be numeric, but these won’t work with
 $print().
    
-
    +
     strict_pos <- function(x) if (all(x > 0)) "yes" else "no"
-fit$summary(variables = NULL, "Strictly Positive" = strict_pos)
-# fit$print(variables = NULL, "Strictly Positive" = strict_pos)
    
-
       variable Strictly Positive
-1      lp__                no
-2        mu                no
-3       tau               yes
-4  theta[1]                no
-5  theta[2]                no
-6  theta[3]                no
-7  theta[4]                no
-8  theta[5]                no
-9  theta[6]                no
-10 theta[7]                no
-11 theta[8]                no
    
+fit$summary(variables = NULL, "Strictly Positive" = strict_pos)
    
+
    
[38;5;246m# A tibble: 11 × 2
[39m
+   variable `Strictly Positive`
+   
[3m
[38;5;246m<chr>
[39m
[23m    
[3m
[38;5;246m<chr>
[39m
[23m              
+
[38;5;250m 1
[39m lp__     no                 
+
[38;5;250m 2
[39m mu       no                 
+
[38;5;250m 3
[39m tau      yes                
+
[38;5;250m 4
[39m theta[1] no                 
+
[38;5;250m 5
[39m theta[2] no                 
+
[38;5;250m 6
[39m theta[3] no                 
+
[38;5;250m 7
[39m theta[4] no                 
+
[38;5;250m 8
[39m theta[5] no                 
+
[38;5;250m 9
[39m theta[6] no                 
+
[38;5;250m10
[39m theta[7] no                 
+
[38;5;250m11
[39m theta[8] no                 
    
+
    +# fit$print(variables = NULL, "Strictly Positive" = strict_pos)
    
 
    For more information, see posterior::summarise_draws(),
 which is called by $summary().
    
 
    
@@ -286,49 +299,49 @@ 
    Extracting posterior draws/samplesdraws_array and
 draws_df formats, but the posterior
 package supports other useful formats as well.
    
-
    +
-
     'draws_array' num [1:1000, 1:4, 1:11] -66.1 -68.2 -67.1 -62.4 -65.6 ...
+
     'draws_array' num [1:1000, 1:4, 1:11] -60.1 -61.2 -56.1 -57.3 -60.4 ...
  - attr(*, "dimnames")=List of 3
   ..$ iteration: chr [1:1000] "1" "2" "3" "4" ...
   ..$ chain    : chr [1:4] "1" "2" "3" "4"
   ..$ variable : chr [1:11] "lp__" "mu" "tau" "theta[1]" ...
    
-
    +
     # draws x variables data frame
 draws_df <- fit$draws(format = "df")
 str(draws_df)
    
 
    draws_df [4,000 × 14] (S3: draws_df/draws/tbl_df/tbl/data.frame)
- $ lp__      : num [1:4000] -66.1 -68.2 -67.1 -62.4 -65.6 ...
- $ mu        : num [1:4000] -2.42 9.44 2.99 2.91 6.73 ...
- $ tau       : num [1:4000] 12.21 6.46 17.66 8.04 8.8 ...
- $ theta[1]  : num [1:4000] 5.57 11.03 -2.77 1.5 8.91 ...
- $ theta[2]  : num [1:4000] 6.97 3.31 6.77 12.84 5.79 ...
- $ theta[3]  : num [1:4000] 8.21 15.21 -8.08 -5.34 -19.54 ...
- $ theta[4]  : num [1:4000] 19.75 19.47 -7.42 -5.76 7.54 ...
- $ theta[5]  : num [1:4000] -4.12 -5.77 6.01 5.63 -3.23 ...
- $ theta[6]  : num [1:4000] -4.03 2.55 2.99 2.86 15.21 ...
- $ theta[7]  : num [1:4000] -0.186 -2.004 10.11 7.803 14.427 ...
- $ theta[8]  : num [1:4000] 0.0702 -3.005 11.0116 14.5279 14.1928 ...
+ $ lp__      : num [1:4000] -60.1 -61.2 -56.1 -57.3 -60.4 ...
+ $ mu        : num [1:4000] 2.61848 0.7849 8.10256 6.57364 -0.00287 ...
+ $ tau       : num [1:4000] 5.08 7.18 4.03 3.89 7.07 ...
+ $ theta[1]  : num [1:4000] 10.98 9.64 5.26 11.34 13.75 ...
+ $ theta[2]  : num [1:4000] -2.54 -3.89 10.58 2.63 10.17 ...
+ $ theta[3]  : num [1:4000] -4.73 -3.78 7.2 4.05 4.26 ...
+ $ theta[4]  : num [1:4000] 5.14 10.67 7.18 4.7 2.94 ...
+ $ theta[5]  : num [1:4000] -5.56 1.63 1.64 3.55 3.04 ...
+ $ theta[6]  : num [1:4000] 1.153 3.155 12.283 -2.088 -0.133 ...
+ $ theta[7]  : num [1:4000] 4.064 0.872 10.52 5.397 7.21 ...
+ $ theta[8]  : num [1:4000] 8.65 -5.18 9.86 5.43 1.18 ...
  $ .chain    : int [1:4000] 1 1 1 1 1 1 1 1 1 1 ...
  $ .iteration: int [1:4000] 1 2 3 4 5 6 7 8 9 10 ...
  $ .draw     : int [1:4000] 1 2 3 4 5 6 7 8 9 10 ...
    
-
    +
     print(draws_df)
    
 
    # A draws_df: 1000 iterations, 4 chains, and 11 variables
-   lp__   mu  tau theta[1] theta[2] theta[3] theta[4] theta[5]
-1   -66 -2.4 12.2      5.6     6.97      8.2    19.75    -4.12
-2   -68  9.4  6.5     11.0     3.31     15.2    19.47    -5.77
-3   -67  3.0 17.7     -2.8     6.77     -8.1    -7.42     6.01
-4   -62  2.9  8.0      1.5    12.84     -5.3    -5.76     5.63
-5   -66  6.7  8.8      8.9     5.79    -19.5     7.54    -3.23
-6   -64  5.3 11.4     18.5    13.37     -1.4    15.97    -0.61
-7   -60  7.3  9.1      8.6     7.82      3.5     0.34    -2.00
-8   -60  6.3  8.5      7.7     0.51      7.5    -0.99     1.51
-9   -59  1.9  6.9      3.0     8.63      1.4     3.70     4.72
-10  -63  9.1  9.3     16.0     5.77      3.9     4.14   -10.34
+   lp__      mu tau theta[1] theta[2] theta[3] theta[4] theta[5]
+1   -60  2.6185 5.1     11.0     -2.5     -4.7      5.1    -5.56
+2   -61  0.7849 7.2      9.6     -3.9     -3.8     10.7     1.63
+3   -56  8.1026 4.0      5.3     10.6      7.2      7.2     1.64
+4   -57  6.5736 3.9     11.3      2.6      4.1      4.7     3.55
+5   -60 -0.0029 7.1     13.7     10.2      4.3      2.9     3.04
+6   -59  3.7000 4.5     10.2      7.6     -2.3      1.2     2.20
+7   -62  5.0756 8.0     11.3     10.9      5.1     14.6     0.37
+8   -63 14.5222 6.3     19.5     16.0      8.3      2.0    18.21
+9   -58 11.0878 4.0     16.8     12.8     10.8      7.9     3.32
+10  -56 10.4559 4.2      9.6     15.2     12.4      8.2    10.41
 # ... with 3990 more draws, and 3 more variables
 # ... hidden reserved variables {'.chain', '.iteration', '.draw'}
    
 
    To convert an existing draws object to a different format use the
diff --git a/docs/articles/profiling.html b/docs/articles/profiling.html
index 0bb97c2a..613e6d86 100644
--- a/docs/articles/profiling.html
+++ b/docs/articles/profiling.html
@@ -40,7 +40,7 @@
       
       
         cmdstanr
-        0.6.0
+        0.7.0
       
     
    
 
@@ -250,11 +250,11 @@ 
    Accessing the profiling info
 fit$profiles()
    
 
    [[1]]
         name   thread_id total_time forward_time reverse_time chain_stack
-1 likelihood 0x102122e00 0.71089600   0.58314100    0.1277560       51969
-2     priors 0x102122e00 0.00482875   0.00293865    0.0018901       34646
+1 likelihood 0x10cc54e00 0.76348000   0.62696000   0.13652000       51132
+2     priors 0x10cc54e00 0.00513667   0.00296958   0.00216709       34088
   no_chain_stack autodiff_calls no_autodiff_calls
-1       34646000          17323                 1
-2              0          17323                 1
    
+1       34105044          17044                 1
+2          34088          17044                 1
    
 
    The total_time column is the total time spent inside a
 given profile statement. It is clear that the vast majority of time is
 spent in the likelihood function.
    
@@ -297,11 +297,11 @@ 
    Comparing to a faster versio
 fit_glm$profiles()
    
 
    [[1]]
         name   thread_id total_time forward_time reverse_time chain_stack
-1 likelihood 0x1066bde00 0.45516500   0.45357200   0.00159287       17695
-2     priors 0x1066bde00 0.00399743   0.00242302   0.00157441       35390
+1 likelihood 0x115a87e00 0.39845200   0.39713500   0.00131726       49845
+2     priors 0x115a87e00 0.00381033   0.00234721   0.00146311       33230
   no_chain_stack autodiff_calls no_autodiff_calls
-1              0          17695                 1
-2              0          17695                 1
    
+1          16615          16615                 1
+2          33230          16615                 1
    
 
    We can see from the total_time column that this is much
 faster than the previous model.
    
 
    
@@ -324,7 +324,7 @@ 
    Per-gradient timings, and memory
 profile_chain_1 <- fit$profiles()[[1]]
 per_gradient_timing <- profile_chain_1$total_time/profile_chain_1$autodiff_calls
 print(per_gradient_timing) # two elements for the two profile statements in the model
    
-
    [1] 4.103770e-05 2.787479e-07
    
+
    [1] 4.479465e-05 3.013770e-07
    
 
    
 
    
 
    Accessing and saving the profile files
@@ -335,7 +335,7 @@ 
    Accessing and saving the profile
 $profile_files().
    
 
     fit$profile_files()
    
-
    [1] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmppMVUoY/model_6580008f67848265f3dfd0e7ae3b0600-profile-202307251456-1-5100ca.csv"
    
+
    [1] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpI4doTK/model_6580008f67848265f3dfd0e7ae3b0600-profile-202311081036-1-4a8018.csv"
    
 
    These can be saved to a more permanent location with the
 $save_profile_files() method.
    
 
    diff --git a/docs/articles/r-markdown.html b/docs/articles/r-markdown.html
index c403854e..23c674be 100644
--- a/docs/articles/r-markdown.html
+++ b/docs/articles/r-markdown.html
@@ -40,7 +40,7 @@
       
       
         cmdstanr
-        0.6.1
+        0.7.0
       
     
    
 
@@ -229,7 +229,7 @@ 
    Example#> 
 #> All 4 chains finished successfully.
 #> Mean chain execution time: 0.0 seconds.
-#> Total execution time: 0.6 seconds.
+#> Total execution time: 0.7 seconds.
 
 print(fit)
 #>  variable  mean median   sd  mad    q5   q95 rhat ess_bulk ess_tail
diff --git a/docs/authors.html b/docs/authors.html
index 3b3e08e8..8c3fd645 100644
--- a/docs/authors.html
+++ b/docs/authors.html
@@ -17,7 +17,7 @@
       
       
         cmdstanr
-        0.6.1
+        0.7.0
       
     
    
 
@@ -139,6 +139,14 @@ 
    Authors
    
           
    Jacob Socolar. Contributor. 
           
    
         
+        
  • 
+          
    Martin Modrák. Contributor. 
+          
    
+        
    • 
+        
  • 
+          
    Steve Bronder. Contributor. 
+          
    
+        
    • 
       
    
     
    
     
    
diff --git a/docs/index.html b/docs/index.html
index 12811cad..cc3f7698 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -45,7 +45,7 @@
       
       
         cmdstanr
-        0.6.1
+        0.7.0
       
     
    
 
@@ -184,7 +184,7 @@ 
    Installing CmdStan
 
    Contributing
 
    
-
    There is a lot of work still to be done and contributions are very welcome! If you are interested in contributing please comment on an open issue or open a new one if none are applicable.
    
+
    There is a lot of work still to be done and we welcome contributions from anyone! If you are interested in contributing please comment on an open issue or open a new one if none are applicable.
    
 
    
 
    
 
    License
diff --git a/docs/news/index.html b/docs/news/index.html
index 526e35b5..64016e37 100644
--- a/docs/news/index.html
+++ b/docs/news/index.html
@@ -17,7 +17,7 @@
       
       
         cmdstanr
-        0.6.1
+        0.7.0
       
     
    
 
@@ -102,6 +102,29 @@ 
    Changelog 
    
       Source: NEWS.md
     
    
 
+    
    
+
    cmdstanr 0.7.0
    
+
    
+
    Major new features
    
+
    
+
    
+
    Other improvements and bug fixes
    
+
    • Add missing link to diagnose method in CmdStanModel doc by @jgabry in #833
      • 
+
    • Improvements to compile tests by @martinmodrak in #836
      • 
+
    • Changed the delay behavior in wsl_installed by @martinmodrak in #839
      • 
+
    • Update array syntax in website vignette by @andrjohns in #841
      • 
+
    • Compatibility fixes for cmdstan 2.33+ by @jgabry in #843
      • 
+
    • Suggest format method after error due to old syntax by @jgabry in #852
      • 
+
    • Clarifications in R-markdown vignette by @jgcolman in #854
      • 
+
    • Update linux/wsl detection for install arch by @andrjohns in #856
      • 
+
    • Fix handling of single-length inits for containers by @andrjohns in #857
      • 
+
    • Add support/tests for exposing functions with tuples by @andrjohns in #860
      • 
+
    • Add support/tests for exporting functions with complex types by @andrjohns in #861
      • 
+
    • Add option for installing from release archive by @andrjohns in #866
      • 
+
    
+
    
     
    
 
    cmdstanr 0.6.1
    
 
    • Store return codes instead of always querying exit status by @jgabry in #798
      • 
diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml
index 39643d5e..2af8a222 100644
--- a/docs/pkgdown.yml
+++ b/docs/pkgdown.yml
@@ -3,14 +3,12 @@ pkgdown: 2.0.7
 pkgdown_sha: ~
 articles:
   opencl: articles-online-only/opencl.html
-  settings-knitr: children/settings-knitr.html
   cmdstanr-internals: cmdstanr-internals.html
   cmdstanr: cmdstanr.html
-  deprecations: deprecations.html
   posterior: posterior.html
   profiling: profiling.html
   r-markdown: r-markdown.html
-last_built: 2023-07-25T20:34Z
+last_built: 2023-11-08T17:25Z
 urls:
   reference: https://mc-stan.org/cmdstanr/reference
   article: https://mc-stan.org/cmdstanr/articles
diff --git a/docs/pull_request_template.html b/docs/pull_request_template.html
index 8c12eee3..dabe9968 100644
--- a/docs/pull_request_template.html
+++ b/docs/pull_request_template.html
@@ -17,7 +17,7 @@
       
       
         cmdstanr
-        0.6.1
+        0.7.0
       
     
    
 
diff --git a/docs/reference/CmdStanDiagnose.html b/docs/reference/CmdStanDiagnose.html
index 4046c268..18388338 100644
--- a/docs/reference/CmdStanDiagnose.html
+++ b/docs/reference/CmdStanDiagnose.html
@@ -18,7 +18,7 @@
       
       
         cmdstanr
-        0.6.0
+        0.7.0
       
     
    
 
@@ -123,8 +123,10 @@ 
    See also
    
 
  • CmdStan User’s Guide: mc-stan.org/docs/cmdstan-guide
    • 
 
    Other fitted model objects: 
 CmdStanGQ,
+CmdStanLaplace,
 CmdStanMCMC,
 CmdStanMLE,
+CmdStanPathfinder,
 CmdStanVB
    
     
    
 
@@ -135,11 +137,11 @@ 
    Examples
    
 
 # retrieve the gradients
 test$gradients()
-#>   param_idx     value    model finite_diff        error
-#> 1         0 -0.185026 11.68660    11.68660 -2.89634e-09
-#> 2         1 -1.579220 12.33760    12.33760  2.86842e-08
-#> 3         2 -0.391481  4.28029     4.28029 -2.55717e-08
-#> 4         3 -0.580364 18.08200    18.08200 -1.15854e-08
+#>   param_idx     value     model finite_diff        error
+#> 1         0  0.797265  -8.06234    -8.06234 -9.72013e-09
+#> 2         1  0.216729 -15.64540   -15.64540  1.12721e-08
+#> 3         2 -0.207005  -4.61097    -4.61097 -5.25634e-09
+#> 4         3  1.118110  -6.60883    -6.60883 -2.24051e-08
 # }
 
 
    
diff --git a/docs/reference/CmdStanGQ.html b/docs/reference/CmdStanGQ.html
index 24ddc1db..28c7f7a4 100644
--- a/docs/reference/CmdStanGQ.html
+++ b/docs/reference/CmdStanGQ.html
@@ -19,7 +19,7 @@
       
       
         cmdstanr
-        0.6.0
+        0.7.0
    @@ -141,8 +141,10 @@

    See also

  • CmdStan User’s Guide: mc-stan.org/docs/cmdstan-guide

    • Other fitted model objects: CmdStanDiagnose, +CmdStanLaplace, CmdStanMCMC, CmdStanMLE, +CmdStanPathfinder, CmdStanVB

    @@ -153,7 +155,7 @@

    Examples

    mcmc_program <- write_stan_file( "data { int<lower=0> N; - int<lower=0,upper=1> y[N]; + array[N] int<lower=0,upper=1> y; } parameters { real<lower=0,upper=1> theta; @@ -163,11 +165,6 @@

    Examples

    }" ) mod_mcmc <- cmdstan_model(mcmc_program) -#> Warning in '/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpFBtN6X/model-16218796affe.stan', line 3, column 4: Declaration -#> of arrays by placing brackets after a variable name is deprecated and -#> will be removed in Stan 2.33.0. Instead use the array keyword before the -#> type. This can be changed automatically using the auto-format flag to -#> stanc data <- list(N = 10, y = c(1,1,0,0,0,1,0,1,0,0)) fit_mcmc <- mod_mcmc$sample(data = data, seed = 123, refresh = 0) @@ -188,27 +185,17 @@

    Examples

    gq_program <- write_stan_file( "data { int<lower=0> N; - int<lower=0,upper=1> y[N]; + array[N] int<lower=0,upper=1> y; } parameters { real<lower=0,upper=1> theta; } generated quantities { - int y_rep[N] = bernoulli_rng(rep_vector(theta, N)); + array[N] int y_rep = bernoulli_rng(rep_vector(theta, N)); }" ) mod_gq <- cmdstan_model(gq_program) -#> Warning in '/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpFBtN6X/model-162182f764088.stan', line 3, column 4: Declaration -#> of arrays by placing brackets after a variable name is deprecated and -#> will be removed in Stan 2.33.0. Instead use the array keyword before the -#> type. This can be changed automatically using the auto-format flag to -#> stanc -#> Warning in '/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpFBtN6X/model-162182f764088.stan', line 9, column 4: Declaration -#> of arrays by placing brackets after a variable name is deprecated and -#> will be removed in Stan 2.33.0. Instead use the array keyword before the -#> type. This can be changed automatically using the auto-format flag to -#> stanc fit_gq <- mod_gq$generate_quantities(fit_mcmc, data = data, seed = 123) #> Running standalone generated quantities after 4 MCMC chains, 1 chain at a time ... #> @@ -228,7 +215,7 @@

    Examples

    #> ..$ variable : chr [1:10] "y_rep[1]" "y_rep[2]" "y_rep[3]" "y_rep[4]" ... library(posterior) -#> This is posterior version 1.4.1 +#> This is posterior version 1.5.0 #> #> Attaching package: ‘posterior’ #> The following objects are masked from ‘package:stats’: diff --git a/docs/reference/CmdStanLaplace.html b/docs/reference/CmdStanLaplace.html new file mode 100644 index 00000000..56d9c8d3 --- /dev/null +++ b/docs/reference/CmdStanLaplace.html @@ -0,0 +1,174 @@ + +CmdStanLaplace objects — CmdStanLaplace • cmdstanr + + +
    +
    + + + +
    +
    +
    +

    CmdStanLaplace objects

    + Source: R/fit.R + +
    + +
    +

    A CmdStanLaplace object is the fitted model object returned by the +$laplace() method of a +CmdStanModel object.

    +
    + + +
    +

    Methods

    +

    CmdStanLaplace objects have the following associated methods, +all of which have their own (linked) documentation pages.

    +

    Extract contents of fitted model object

    +
    MethodDescription
    $draws()Return approximate posterior draws as a draws_matrix.
    $mode()Return the mode as a CmdStanMLE object.
    $lp()Return the total log probability density (target) computed in the model block of the Stan program.
    $lp_approx()Return the log density of the approximation to the posterior.
    $init()Return user-specified initial values.
    $metadata()Return a list of metadata gathered from the CmdStan CSV files.
    $code()Return Stan code as a character vector.
    + +
    +

    Summarize inferences

    +
    MethodDescription
    $summary()Run posterior::summarise_draws().
    + +
    +

    Save fitted model object and temporary files

    +
    MethodDescription
    $save_object()Save fitted model object to a file.
    $save_output_files()Save output CSV files to a specified location.
    $save_data_file()Save JSON data file to a specified location.
    $save_latent_dynamics_files()Save diagnostic CSV files to a specified location.
    + +
    +

    Report run times, console output, return codes

    +
    MethodDescription
    $time()Report the run time of the Laplace sampling step.
    $output()Pretty print the output that was printed to the console.
    $return_codes()Return the return codes from the CmdStan runs.
    + +
    +
    +

    See also

    +

    The CmdStanR website +(mc-stan.org/cmdstanr) for online +documentation and tutorials.

    +

    The Stan and CmdStan documentation:

    Other fitted model objects: +CmdStanDiagnose, +CmdStanGQ, +CmdStanMCMC, +CmdStanMLE, +CmdStanPathfinder, +CmdStanVB

    +
    + +
    + +
    + + +
    +

    Developed by Jonah Gabry, Rok Češnovar, Andrew Johnson.

    +
    + +
    +

    Site built with pkgdown 2.0.7.

    +
    + +
    + + + + + + + + diff --git a/docs/reference/CmdStanMCMC.html b/docs/reference/CmdStanMCMC.html index e7ecaec5..4b2ed95b 100644 --- a/docs/reference/CmdStanMCMC.html +++ b/docs/reference/CmdStanMCMC.html @@ -20,7 +20,7 @@ cmdstanr - 0.6.0 + 0.7.0 @@ -148,7 +148,9 @@

    See also

    Other fitted model objects: CmdStanDiagnose, CmdStanGQ, +CmdStanLaplace, CmdStanMLE, +CmdStanPathfinder, CmdStanVB

    diff --git a/docs/reference/CmdStanMLE.html b/docs/reference/CmdStanMLE.html index 1eb913bf..e2d322da 100644 --- a/docs/reference/CmdStanMLE.html +++ b/docs/reference/CmdStanMLE.html @@ -18,7 +18,7 @@ cmdstanr - 0.6.0 + 0.7.0 @@ -144,7 +144,9 @@

    See also

    Other fitted model objects: CmdStanDiagnose, CmdStanGQ, +CmdStanLaplace, CmdStanMCMC, +CmdStanPathfinder, CmdStanVB

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b/docs/reference/CmdStanModel.html @@ -20,7 +20,7 @@ cmdstanr - 0.6.0 + 0.7.0 @@ -131,7 +131,7 @@

    Diagnostics

    Model fitting

    -
    MethodDescription
    $sample()Run CmdStan's "sample" method, return CmdStanMCMC object.
    $sample_mpi()Run CmdStan's "sample" method with MPI, return CmdStanMCMC object.
    $optimize()Run CmdStan's "optimize" method, return CmdStanMLE object.
    $variational()Run CmdStan's "variational" method, return CmdStanVB object.
    $generate_quantities()Run CmdStan's "generate quantities" method, return CmdStanGQ object.
    +
    MethodDescription
    $sample()Run CmdStan's "sample" method, return CmdStanMCMC object.
    $sample_mpi()Run CmdStan's "sample" method with MPI, return CmdStanMCMC object.
    $optimize()Run CmdStan's "optimize" method, return CmdStanMLE object.
    $variational()Run CmdStan's "variational" method, return CmdStanVB object.
    $pathfinder()Run CmdStan's "pathfinder" method, return CmdStanPathfinder object.
    $generate_quantities()Run CmdStan's "generate quantities" method, return CmdStanGQ object.
    @@ -149,15 +149,6 @@

    Examples

    # \dontrun{
 library(cmdstanr)
 library(posterior)
-#> This is posterior version 1.4.1.9000
-#> 
-#> Attaching package: ‘posterior’
-#> The following objects are masked from ‘package:stats’:
-#> 
-#>     mad, sd, var
-#> The following objects are masked from ‘package:base’:
-#> 
-#>     %in%, match
 library(bayesplot)
 #> This is bayesplot version 1.10.0
 #> - Online documentation and vignettes at mc-stan.org/bayesplot
@@ -177,13 +168,12 @@ 
    Examples
    
 # Otherwise use the `path` argument to specify the location of your
 # CmdStan installation.)
 set_cmdstan_path(path = NULL)
-#> CmdStan path set to: /Users/jgabry/.cmdstan/cmdstan-2.32.2
+#> CmdStan path set to: /Users/jgabry/.cmdstan/cmdstan-2.33.1
 
 # Create a CmdStanModel object from a Stan program,
 # here using the example model that comes with CmdStan
 file <- file.path(cmdstan_path(), "examples/bernoulli/bernoulli.stan")
 mod <- cmdstan_model(file)
-#> Model executable is up to date!
 mod$print()
 #> data {
 #>   int<lower=0> N;
@@ -269,6 +259,18 @@ 
    Examples
    
 #> 1 lp__     -7.30  -7.03  0.721 0.380 -8.82   -6.75   1.00     902.    1006.
 #> 2 theta     0.247  0.233 0.122 0.129  0.0786  0.470  1.00     762.     712.
 
+# Check sampling diagnostics
+fit_mcmc$diagnostic_summary()
+#> $num_divergent
+#> [1] 0 0
+#> 
+#> $num_max_treedepth
+#> [1] 0 0
+#> 
+#> $ebfmi
+#> [1] 1.017555 1.250490
+#> 
+
 # Get posterior draws
 draws <- fit_mcmc$draws()
 print(draws)
@@ -317,51 +319,8 @@ 
    Examples
    
 #> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
 
 
-# Call CmdStan's diagnose and stansummary utilities
-fit_mcmc$cmdstan_diagnose()
-#> Processing csv files: /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGdLwVH/bernoulli-202308230921-1-6956c8.csv, /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGdLwVH/bernoulli-202308230921-2-6956c8.csv
-#> 
-#> Checking sampler transitions treedepth.
-#> Treedepth satisfactory for all transitions.
-#> 
-#> Checking sampler transitions for divergences.
-#> No divergent transitions found.
-#> 
-#> Checking E-BFMI - sampler transitions HMC potential energy.
-#> E-BFMI satisfactory.
-#> 
-#> Effective sample size satisfactory.
-#> 
-#> Split R-hat values satisfactory all parameters.
-#> 
-#> Processing complete, no problems detected.
-fit_mcmc$cmdstan_summary()
-#> Inference for Stan model: bernoulli_model
-#> 2 chains: each with iter=(1000,1000); warmup=(0,0); thin=(1,1); 2000 iterations saved.
-#> 
-#> Warmup took (0.0040, 0.0040) seconds, 0.0080 seconds total
-#> Sampling took (0.011, 0.012) seconds, 0.023 seconds total
-#> 
-#>                 Mean     MCSE  StdDev     5%   50%   95%  N_Eff  N_Eff/s    R_hat
-#> 
-#> lp__            -7.3  2.6e-02    0.72   -8.8  -7.0  -6.8    781    33958      1.0
-#> accept_stat__   0.92  8.3e-03    0.13   0.64  0.97   1.0    235    10198  1.0e+00
-#> stepsize__      0.95  7.9e-02   0.079   0.87   1.0   1.0    1.0       44  2.0e+13
-#> treedepth__      1.4  1.1e-02    0.48    1.0   1.0   2.0   1874    81476  1.0e+00
-#> n_leapfrog__     2.5  1.4e-01     1.3    1.0   3.0   3.0     89     3874  1.0e+00
-#> divergent__     0.00      nan    0.00   0.00  0.00  0.00    nan      nan      nan
-#> energy__         7.8  3.6e-02    1.00    6.8   7.5   9.6    775    33684  1.0e+00
-#> 
-#> theta           0.25  4.3e-03    0.12  0.079  0.23  0.47    796    34623      1.0
-#> 
-#> Samples were drawn using hmc with nuts.
-#> For each parameter, N_Eff is a crude measure of effective sample size,
-#> and R_hat is the potential scale reduction factor on split chains (at 
-#> convergence, R_hat=1).
-
 # For models fit using MCMC, if you like working with RStan's stanfit objects
 # then you can create one with rstan::read_stan_csv()
-
 # stanfit <- rstan::read_stan_csv(fit_mcmc$output_files())
 
 
@@ -375,7 +334,6 @@ 
    Examples
    
 #> Optimization terminated normally:  
 #>   Convergence detected: relative gradient magnitude is below tolerance 
 #> Finished in  0.1 seconds.
-
 fit_optim$summary()
 #> # A tibble: 2 × 2
 #>   variable estimate
@@ -383,16 +341,57 @@ 
    Examples
    
 #> 1 lp__        -5.00
 #> 2 theta        0.2 
 
+# Run 'optimize' again with 'jacobian=TRUE' and then draw from laplace approximation
+# to the posterior
+fit_optim <- mod$optimize(data = my_data_file, jacobian = TRUE)
+#> Initial log joint probability = -6.94684 
+#>     Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes  
+#>        4      -6.74802   0.000319208   6.44211e-05      0.9206      0.9206        7    
+#> Optimization terminated normally:  
+#>   Convergence detected: relative gradient magnitude is below tolerance 
+#> Finished in  0.1 seconds.
+fit_laplace <- mod$laplace(data = my_data_file, mode = fit_optim, draws = 2000)
+#> Calculating Hessian 
+#> Calculating inverse of Cholesky factor 
+#> Generating draws 
+#> iteration: 0 
+#> iteration: 100 
+#> iteration: 200 
+#> iteration: 300 
+#> iteration: 400 
+#> iteration: 500 
+#> iteration: 600 
+#> iteration: 700 
+#> iteration: 800 
+#> iteration: 900 
+#> iteration: 1000 
+#> iteration: 1100 
+#> iteration: 1200 
+#> iteration: 1300 
+#> iteration: 1400 
+#> iteration: 1500 
+#> iteration: 1600 
+#> iteration: 1700 
+#> iteration: 1800 
+#> iteration: 1900 
+#> Finished in  0.1 seconds.
+fit_laplace$summary()
+#> # A tibble: 3 × 7
+#>   variable      mean median    sd   mad     q5      q95
+#>   <chr>        <dbl>  <dbl> <dbl> <dbl>  <dbl>    <dbl>
+#> 1 lp__        -7.23  -6.96  0.692 0.298 -8.50  -6.75   
+#> 2 lp_approx__ -0.490 -0.226 0.691 0.313 -1.88  -0.00180
+#> 3 theta        0.267  0.249 0.122 0.119  0.102  0.497  
 
-# Run 'variational' method to approximate the posterior (default is meanfield ADVI)
+# Run 'variational' method to use ADVI to approximate posterior
 fit_vb <- mod$variational(data = stan_data, seed = 123)
 #> ------------------------------------------------------------ 
 #> EXPERIMENTAL ALGORITHM: 
 #>   This procedure has not been thoroughly tested and may be unstable 
 #>   or buggy. The interface is subject to change. 
 #> ------------------------------------------------------------ 
-#> Gradient evaluation took 5e-06 seconds 
-#> 1000 transitions using 10 leapfrog steps per transition would take 0.05 seconds. 
+#> Gradient evaluation took 6e-06 seconds 
+#> 1000 transitions using 10 leapfrog steps per transition would take 0.06 seconds. 
 #> Adjust your expectations accordingly! 
 #> Begin eta adaptation. 
 #> Iteration:   1 / 250 [  0%]  (Adaptation) 
@@ -409,7 +408,6 @@ 
    Examples
    
 #> Drawing a sample of size 1000 from the approximate posterior...  
 #> COMPLETED. 
 #> Finished in  0.1 seconds.
-
 fit_vb$summary()
 #> # A tibble: 3 × 7
 #>   variable      mean median    sd   mad     q5      q95
@@ -417,12 +415,40 @@ 
    Examples
    
 #> 1 lp__        -7.18  -6.94  0.588 0.259 -8.36  -6.75   
 #> 2 lp_approx__ -0.515 -0.221 0.692 0.303 -2.06  -0.00257
 #> 3 theta        0.263  0.246 0.115 0.113  0.106  0.481  
-
-# Plot approximate posterior using bayesplot
 mcmc_hist(fit_vb$draws("theta"))
 #> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
 
 
+# Run 'pathfinder' method, a new alternative to the variational method
+fit_pf <- mod$pathfinder(data = stan_data, seed = 123)
+#> Path [1] :Initial log joint density = -11.008832 
+#> Path [1] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               5      -6.748e+00      9.383e-04   1.391e-05    1.000e+00  1.000e+00       126 -6.264e+00 -6.264e+00                   
+#> Path [1] :Best Iter: [3] ELBO (-6.195408) evaluations: (126) 
+#> Path [2] :Initial log joint density = -7.318450 
+#> Path [2] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               4      -6.748e+00      5.414e-03   1.618e-04    1.000e+00  1.000e+00       101 -6.251e+00 -6.251e+00                   
+#> Path [2] :Best Iter: [3] ELBO (-6.229174) evaluations: (101) 
+#> Path [3] :Initial log joint density = -12.374612 
+#> Path [3] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               5      -6.748e+00      1.419e-03   2.837e-05    1.000e+00  1.000e+00       126 -6.199e+00 -6.199e+00                   
+#> Path [3] :Best Iter: [5] ELBO (-6.199185) evaluations: (126) 
+#> Path [4] :Initial log joint density = -13.009824 
+#> Path [4] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               5      -6.748e+00      1.677e-03   3.885e-05    1.000e+00  1.000e+00       126 -6.173e+00 -6.173e+00                   
+#> Path [4] :Best Iter: [5] ELBO (-6.172860) evaluations: (126) 
+#> Total log probability function evaluations:4379 
+#> Finished in  0.1 seconds.
+fit_pf$summary()
+#> # A tibble: 3 × 7
+#>   variable      mean median    sd   mad      q5    q95
+#>   <chr>        <dbl>  <dbl> <dbl> <dbl>   <dbl>  <dbl>
+#> 1 lp_approx__ -1.08  -0.728 0.886 0.304 -2.71   -0.511
+#> 2 lp__        -7.26  -6.96  0.738 0.297 -8.72   -6.75 
+#> 3 theta        0.249  0.230 0.120 0.121  0.0854  0.471
+mcmc_hist(fit_pf$draws("theta"))
+#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
+
 
 # Specifying initial values as a function
 fit_mcmc_w_init_fun <- mod$sample(
@@ -490,7 +516,7 @@ 
    Examples
    
 #> 
 #> Both chains finished successfully.
 #> Mean chain execution time: 0.0 seconds.
-#> Total execution time: 0.3 seconds.
+#> Total execution time: 0.4 seconds.
 #> 
 fit_optim_w_init_list <- mod$optimize(
   data = stan_data,
diff --git a/docs/reference/CmdStanPathfinder.html b/docs/reference/CmdStanPathfinder.html
new file mode 100644
index 00000000..3da6af49
--- /dev/null
+++ b/docs/reference/CmdStanPathfinder.html
@@ -0,0 +1,174 @@
+
+CmdStanPathfinder objects — CmdStanPathfinder • cmdstanr
+    
+
+    
    
+      
    
+
+      
+
+      
    
+  
    
+    
    
+    
    CmdStanPathfinder objects
    
+    Source: R/fit.R
+    
+    
    
+
+    
    
+    
    A CmdStanPathfinder object is the fitted model object returned by the
+$pathfinder() method of a
+CmdStanModel object.
    
+    
    
+
+
+    
    
+    
    Methods
    
+    
    CmdStanPathfinder objects have the following associated methods,
+all of which have their own (linked) documentation pages.
    
+
    Extract contents of fitted model object
    
+
    MethodDescription
    $draws()Return approximate posterior draws as a draws_matrix.
    $lp()Return the total log probability density (target) computed in the model block of the Stan program.
    $lp_approx()Return the log density of the approximation to the posterior.
    $init()Return user-specified initial values.
    $metadata()Return a list of metadata gathered from the CmdStan CSV files.
    $code()Return Stan code as a character vector.
    
+
+
    
+
    Summarize inferences
    
+
    MethodDescription
    $summary()Run posterior::summarise_draws().
    $cmdstan_summary()Run and print CmdStan's bin/stansummary.
    
+
+
    
+
    Save fitted model object and temporary files
    
+
    MethodDescription
    $save_object()Save fitted model object to a file.
    $save_output_files()Save output CSV files to a specified location.
    $save_data_file()Save JSON data file to a specified location.
    $save_latent_dynamics_files()Save diagnostic CSV files to a specified location.
    
+
+
    
+
    Report run times, console output, return codes
    
+
    MethodDescription
    $time()Report the total run time.
    $output()Pretty print the output that was printed to the console.
    $return_codes()Return the return codes from the CmdStan runs.
    
+
+    
    
+    
    
+    
    See also
    
+    
    The CmdStanR website
+(mc-stan.org/cmdstanr) for online
+documentation and tutorials.
    
+
    The Stan and CmdStan documentation:
    Other fitted model objects: 
+CmdStanDiagnose,
+CmdStanGQ,
+CmdStanLaplace,
+CmdStanMCMC,
+CmdStanMLE,
+CmdStanVB
    
+    
    
+
+  
    
+  
+
    
+
+
+      
    
+  
    Developed by Jonah Gabry, Rok Češnovar, Andrew Johnson.
    
+
    
+
+
    
+  
    Site built with pkgdown 2.0.7.
    
+
    
+
+      
    
+
+  
+
+
+  
+
+  
+
diff --git a/docs/reference/CmdStanVB.html b/docs/reference/CmdStanVB.html
index 51c738a3..b3ea8347 100644
--- a/docs/reference/CmdStanVB.html
+++ b/docs/reference/CmdStanVB.html
@@ -19,7 +19,7 @@
       
       
         cmdstanr
-        0.6.0
+        0.7.0
    @@ -146,8 +146,10 @@

    See also

    Other fitted model objects: CmdStanDiagnose, CmdStanGQ, +CmdStanLaplace, CmdStanMCMC, -CmdStanMLE

    +CmdStanMLE, +CmdStanPathfinder

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    Create a draws object from a CmdStanR fitted model object

    # S3 method for CmdStanMLE as_draws(x, ...) +# S3 method for CmdStanLaplace +as_draws(x, ...) + # S3 method for CmdStanVB as_draws(x, ...) # S3 method for CmdStanGQ +as_draws(x, ...) + +# S3 method for CmdStanPathfinder as_draws(x, ...)     @@ -152,41 +158,41 @@

    Examples

    #> #> chain #> iteration 1 2 3 4 -#> 1 -66 -65 -65 -64 -#> 2 -66 -65 -64 -66 -#> 3 -65 -66 -65 -66 -#> 4 -65 -65 -66 -65 -#> 5 -67 -66 -66 -67 +#> 1 -66 -68 -66 -65 +#> 2 -65 -67 -65 -66 +#> 3 -68 -66 -65 -66 +#> 4 -68 -65 -65 -67 +#> 5 -67 -64 -65 -67 #> #> , , variable = alpha #> #> chain -#> iteration 1 2 3 4 -#> 1 0.37 0.21 0.66 0.314 -#> 2 0.34 0.35 0.28 0.748 -#> 3 0.58 0.25 0.41 0.023 -#> 4 0.20 0.37 0.45 0.297 -#> 5 0.24 0.37 0.72 0.328 +#> iteration 1 2 3 4 +#> 1 0.44 0.414 0.71 0.46 +#> 2 0.30 -0.045 0.44 0.17 +#> 3 0.65 0.397 0.14 0.55 +#> 4 0.29 0.644 0.47 0.54 +#> 5 0.53 0.435 0.48 0.20 #> #> , , variable = beta[1] #> #> chain #> iteration 1 2 3 4 -#> 1 -0.54 -0.69 -0.71 -0.55 -#> 2 -0.98 -0.91 -0.67 -0.70 -#> 3 -0.77 -0.30 -0.82 -0.63 -#> 4 -0.41 -0.66 -1.07 -0.75 -#> 5 -0.26 -0.78 -0.72 -0.45 +#> 1 -0.91 -0.17 -0.50 -0.59 +#> 2 -0.57 -0.97 -0.78 -0.65 +#> 3 -0.90 -0.28 -0.38 -0.43 +#> 4 -1.20 -0.58 -0.92 -1.04 +#> 5 -0.59 -0.74 -0.70 -0.98 #> #> , , variable = beta[2] #> #> chain -#> iteration 1 2 3 4 -#> 1 -0.738 -0.427 -0.183 -0.256 -#> 2 -0.545 -0.076 -0.220 -0.433 -#> 3 -0.168 -0.494 -0.400 -0.201 -#> 4 -0.281 -0.029 -0.388 0.072 -#> 5 0.054 -0.460 -0.027 0.122 +#> iteration 1 2 3 4 +#> 1 0.088 -0.192 -0.43 -0.621 +#> 2 -0.515 -0.088 -0.35 -0.591 +#> 3 -0.031 -0.444 -0.11 0.017 +#> 4 -0.653 -0.229 -0.37 0.066 +#> 5 -0.345 -0.277 -0.13 -0.678 #> #> # ... with 995 more iterations, and 101 more variables @@ -194,70 +200,70 @@

    Examples

    posterior::as_draws_rvars(fit) #> # A draws_rvars: 1000 iterations, 4 chains, and 4 variables #> $lp__: rvar<1000,4>[1] mean ± sd: -#> [1] -66 ± 1.4 +#> [1] -66 ± 1.5 #> #> $alpha: rvar<1000,4>[1] mean ± sd: -#> [1] 0.37 ± 0.22 +#> [1] 0.38 ± 0.22 #> #> $beta: rvar<1000,4>[3] mean ± sd: -#> [1] -0.67 ± 0.25 -0.27 ± 0.22 0.67 ± 0.27 +#> [1] -0.67 ± 0.26 -0.27 ± 0.23 0.69 ± 0.27 #> #> $log_lik: rvar<1000,4>[100] mean ± sd: -#> [1] -0.517 ± 0.101 -0.404 ± 0.151 -0.497 ± 0.215 -0.451 ± 0.152 -#> [5] -1.177 ± 0.282 -0.595 ± 0.194 -0.639 ± 0.124 -0.281 ± 0.134 -#> [9] -0.696 ± 0.170 -0.736 ± 0.229 -0.284 ± 0.127 -0.503 ± 0.249 -#> [13] -0.654 ± 0.209 -0.361 ± 0.173 -0.281 ± 0.106 -0.278 ± 0.087 -#> [17] -1.585 ± 0.286 -0.482 ± 0.109 -0.234 ± 0.076 -0.114 ± 0.078 -#> [21] -0.214 ± 0.088 -0.572 ± 0.150 -0.333 ± 0.139 -0.137 ± 0.067 -#> [25] -0.455 ± 0.121 -1.516 ± 0.346 -0.310 ± 0.123 -0.447 ± 0.085 -#> [29] -0.723 ± 0.222 -0.697 ± 0.195 -0.492 ± 0.165 -0.426 ± 0.108 -#> [33] -0.410 ± 0.128 -0.064 ± 0.050 -0.584 ± 0.182 -0.327 ± 0.132 -#> [37] -0.697 ± 0.220 -0.313 ± 0.145 -0.180 ± 0.109 -0.682 ± 0.131 -#> [41] -1.124 ± 0.254 -0.931 ± 0.201 -0.413 ± 0.269 -1.172 ± 0.186 -#> [45] -0.360 ± 0.119 -0.582 ± 0.132 -0.307 ± 0.130 -0.326 ± 0.084 -#> [49] -0.321 ± 0.080 -1.289 ± 0.340 -0.290 ± 0.095 -0.834 ± 0.146 -#> [53] -0.405 ± 0.132 -0.371 ± 0.144 -0.387 ± 0.137 -0.322 ± 0.189 -#> [57] -0.657 ± 0.118 -0.947 ± 0.345 -1.358 ± 0.345 -0.977 ± 0.161 -#> [61] -0.542 ± 0.101 -0.881 ± 0.324 -0.118 ± 0.073 -0.899 ± 0.249 -#> [65] -1.999 ± 0.597 -0.509 ± 0.135 -0.278 ± 0.082 -1.056 ± 0.235 -#> [69] -0.437 ± 0.085 -0.641 ± 0.239 -0.608 ± 0.208 -0.462 ± 0.171 -#> [73] -1.479 ± 0.361 -0.947 ± 0.192 -1.146 ± 0.401 -0.373 ± 0.139 -#> [77] -0.875 ± 0.138 -0.489 ± 0.178 -0.762 ± 0.186 -0.544 ± 0.202 -#> [81] -0.164 ± 0.099 -0.225 ± 0.139 -0.345 ± 0.082 -0.277 ± 0.091 -#> [85] -0.131 ± 0.075 -1.126 ± 0.328 -0.822 ± 0.130 -0.773 ± 0.243 -#> [89] -1.276 ± 0.323 -0.261 ± 0.132 -0.386 ± 0.129 -1.490 ± 0.344 -#> [93] -0.740 ± 0.216 -0.319 ± 0.088 -0.391 ± 0.113 -1.566 ± 0.280 -#> [97] -0.433 ± 0.103 -1.050 ± 0.364 -0.693 ± 0.143 -0.392 ± 0.099 +#> [1] -0.517 ± 0.098 -0.401 ± 0.152 -0.501 ± 0.223 -0.448 ± 0.154 +#> [5] -1.185 ± 0.289 -0.590 ± 0.195 -0.640 ± 0.126 -0.278 ± 0.135 +#> [9] -0.693 ± 0.170 -0.742 ± 0.238 -0.279 ± 0.127 -0.494 ± 0.244 +#> [13] -0.659 ± 0.215 -0.363 ± 0.176 -0.279 ± 0.108 -0.275 ± 0.087 +#> [17] -1.593 ± 0.288 -0.479 ± 0.107 -0.232 ± 0.076 -0.113 ± 0.080 +#> [21] -0.211 ± 0.089 -0.568 ± 0.149 -0.330 ± 0.140 -0.136 ± 0.067 +#> [25] -0.454 ± 0.123 -1.517 ± 0.342 -0.306 ± 0.123 -0.445 ± 0.083 +#> [29] -0.725 ± 0.232 -0.696 ± 0.191 -0.487 ± 0.164 -0.423 ± 0.109 +#> [33] -0.409 ± 0.130 -0.064 ± 0.052 -0.587 ± 0.189 -0.325 ± 0.132 +#> [37] -0.700 ± 0.231 -0.312 ± 0.151 -0.180 ± 0.111 -0.681 ± 0.128 +#> [41] -1.133 ± 0.258 -0.932 ± 0.200 -0.416 ± 0.265 -1.175 ± 0.185 +#> [45] -0.360 ± 0.118 -0.580 ± 0.131 -0.303 ± 0.131 -0.324 ± 0.082 +#> [49] -0.319 ± 0.079 -1.285 ± 0.333 -0.289 ± 0.094 -0.832 ± 0.142 +#> [53] -0.401 ± 0.131 -0.373 ± 0.143 -0.384 ± 0.139 -0.321 ± 0.192 +#> [57] -0.658 ± 0.119 -0.951 ± 0.363 -1.364 ± 0.348 -0.976 ± 0.158 +#> [61] -0.542 ± 0.097 -0.871 ± 0.318 -0.116 ± 0.075 -0.901 ± 0.250 +#> [65] -2.026 ± 0.610 -0.508 ± 0.138 -0.276 ± 0.082 -1.061 ± 0.237 +#> [69] -0.435 ± 0.083 -0.641 ± 0.235 -0.613 ± 0.214 -0.464 ± 0.177 +#> [73] -1.489 ± 0.374 -0.948 ± 0.198 -1.136 ± 0.394 -0.374 ± 0.140 +#> [77] -0.878 ± 0.139 -0.491 ± 0.174 -0.765 ± 0.194 -0.539 ± 0.200 +#> [81] -0.163 ± 0.102 -0.223 ± 0.145 -0.343 ± 0.080 -0.275 ± 0.092 +#> [85] -0.130 ± 0.075 -1.132 ± 0.322 -0.822 ± 0.126 -0.780 ± 0.249 +#> [89] -1.283 ± 0.323 -0.261 ± 0.137 -0.387 ± 0.133 -1.501 ± 0.352 +#> [93] -0.737 ± 0.221 -0.318 ± 0.087 -0.388 ± 0.112 -1.573 ± 0.284 +#> [97] -0.432 ± 0.100 -1.053 ± 0.383 -0.693 ± 0.143 -0.392 ± 0.096 #> posterior::as_draws_list(fit) #> # A draws_list: 1000 iterations, 4 chains, and 105 variables #> #> [chain = 1] #> $lp__ -#> [1] -66 -66 -65 -65 -67 -65 -64 -64 -66 -64 +#> [1] -66 -65 -68 -68 -67 -66 -65 -66 -67 -66 #> #> $alpha -#> [1] 0.37 0.34 0.58 0.20 0.24 0.48 0.36 0.35 0.21 0.35 +#> [1] 0.44 0.30 0.65 0.29 0.53 0.53 0.47 0.38 0.32 0.51 #> #> $`beta[1]` -#> [1] -0.54 -0.98 -0.77 -0.41 -0.26 -0.80 -0.62 -0.74 -0.55 -0.51 +#> [1] -0.91 -0.57 -0.90 -1.20 -0.59 -0.51 -0.36 -0.19 -1.17 -1.02 #> #> $`beta[2]` -#> [1] -0.738 -0.545 -0.168 -0.281 0.054 -0.315 -0.204 -0.103 0.067 -0.446 +#> [1] 0.088 -0.515 -0.031 -0.653 -0.345 -0.646 -0.154 -0.488 -0.013 0.034 #> #> #> [chain = 2] #> $lp__ -#> [1] -65 -65 -66 -65 -66 -68 -66 -68 -65 -65 +#> [1] -68 -67 -66 -65 -64 -65 -65 -64 -65 -65 #> #> $alpha -#> [1] 0.211 0.348 0.245 0.371 0.366 0.050 0.135 -0.055 0.417 0.247 +#> [1] 0.414 -0.045 0.397 0.644 0.435 0.266 0.415 0.369 0.229 0.185 #> #> $`beta[1]` -#> [1] -0.69 -0.91 -0.30 -0.66 -0.78 -0.47 -0.86 -0.85 -0.32 -0.72 +#> [1] -0.17 -0.97 -0.28 -0.58 -0.74 -0.48 -0.83 -0.72 -0.44 -0.58 #> #> $`beta[2]` -#> [1] -0.427 -0.076 -0.494 -0.029 -0.460 0.199 -0.272 0.096 -0.201 -0.249 +#> [1] -0.192 -0.088 -0.444 -0.229 -0.277 -0.314 0.046 -0.240 -0.080 -0.090 #> #> # ... with 990 more iterations, and 2 more chains, and 101 more variables # } diff --git a/docs/reference/as_mcmc.list.html b/docs/reference/as_mcmc.list.html index 62098ec9..f0a3fe21 100644 --- a/docs/reference/as_mcmc.list.html +++ b/docs/reference/as_mcmc.list.html @@ -23,7 +23,7 @@ cmdstanr - 0.6.0 + 0.7.0 diff --git a/docs/reference/cmdstan_default_install_path.html b/docs/reference/cmdstan_default_install_path.html index 45d26239..e2ca8ac8 100644 --- a/docs/reference/cmdstan_default_install_path.html +++ b/docs/reference/cmdstan_default_install_path.html @@ -17,7 +17,7 @@ cmdstanr - 0.6.0 + 0.7.0 diff --git a/docs/reference/cmdstan_default_path.html b/docs/reference/cmdstan_default_path.html index 624f12f6..01f43b73 100644 --- a/docs/reference/cmdstan_default_path.html +++ b/docs/reference/cmdstan_default_path.html @@ -18,7 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b/docs/reference/cmdstan_model.html @@ -23,7 +23,7 @@ cmdstanr - 0.6.0 + 0.7.0 @@ -185,7 +185,7 @@

    Examples

    # Otherwise use the `path` argument to specify the location of your # CmdStan installation.) set_cmdstan_path(path = NULL) -#> CmdStan path set to: /Users/jgabry/.cmdstan/cmdstan-2.32.2 +#> CmdStan path set to: /Users/jgabry/.cmdstan/cmdstan-2.33.1 # Create a CmdStanModel object from a Stan program, # here using the example model that comes with CmdStan @@ -272,10 +272,22 @@

    Examples

    fit_mcmc$summary() #> # A tibble: 2 × 10 #> variable mean median sd mad q5 q95 rhat ess_bulk ess_tail -#> <chr> <num> <num> <num> <num> <num> <num> <num> <num> <num> +#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 lp__ -7.30 -7.03 0.721 0.380 -8.82 -6.75 1.00 902. 1006. #> 2 theta 0.247 0.233 0.122 0.129 0.0786 0.470 1.00 762. 712. +# Check sampling diagnostics +fit_mcmc$diagnostic_summary() +#> $num_divergent +#> [1] 0 0 +#> +#> $num_max_treedepth +#> [1] 0 0 +#> +#> $ebfmi +#> [1] 1.017555 1.250490 +#> + # Get posterior draws draws <- fit_mcmc$draws() print(draws) @@ -324,51 +336,8 @@

    Examples

    #> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`. -# Call CmdStan's diagnose and stansummary utilities -fit_mcmc$cmdstan_diagnose() -#> Processing csv files: /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpFBtN6X/bernoulli-202307251434-1-79f79a.csv, /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpFBtN6X/bernoulli-202307251434-2-79f79a.csv -#> -#> Checking sampler transitions treedepth. -#> Treedepth satisfactory for all transitions. -#> -#> Checking sampler transitions for divergences. -#> No divergent transitions found. -#> -#> Checking E-BFMI - sampler transitions HMC potential energy. -#> E-BFMI satisfactory. -#> -#> Effective sample size satisfactory. -#> -#> Split R-hat values satisfactory all parameters. -#> -#> Processing complete, no problems detected. -fit_mcmc$cmdstan_summary() -#> Inference for Stan model: bernoulli_model -#> 2 chains: each with iter=(1000,1000); warmup=(0,0); thin=(1,1); 2000 iterations saved. -#> -#> Warmup took (0.0040, 0.0030) seconds, 0.0070 seconds total -#> Sampling took (0.011, 0.011) seconds, 0.022 seconds total -#> -#> Mean MCSE StdDev 5% 50% 95% N_Eff N_Eff/s R_hat -#> -#> lp__ -7.3 2.6e-02 0.72 -8.8 -7.0 -6.8 781 35502 1.0 -#> accept_stat__ 0.92 8.3e-03 0.13 0.64 0.97 1.0 235 10662 1.0e+00 -#> stepsize__ 0.95 7.9e-02 0.079 0.87 1.0 1.0 1.0 46 2.0e+13 -#> treedepth__ 1.4 1.1e-02 0.48 1.0 1.0 2.0 1874 85179 1.0e+00 -#> n_leapfrog__ 2.5 1.4e-01 1.3 1.0 3.0 3.0 89 4050 1.0e+00 -#> divergent__ 0.00 nan 0.00 0.00 0.00 0.00 nan nan nan -#> energy__ 7.8 3.6e-02 1.00 6.8 7.5 9.6 775 35215 1.0e+00 -#> -#> theta 0.25 4.3e-03 0.12 0.079 0.23 0.47 796 36197 1.0 -#> -#> Samples were drawn using hmc with nuts. -#> For each parameter, N_Eff is a crude measure of effective sample size, -#> and R_hat is the potential scale reduction factor on split chains (at -#> convergence, R_hat=1). - # For models fit using MCMC, if you like working with RStan's stanfit objects # then you can create one with rstan::read_stan_csv() - # stanfit <- rstan::read_stan_csv(fit_mcmc$output_files()) @@ -382,16 +351,56 @@

    Examples

    #> Optimization terminated normally: #> Convergence detected: relative gradient magnitude is below tolerance #> Finished in 0.1 seconds. - fit_optim$summary() #> # A tibble: 2 × 2 #> variable estimate -#> <chr> <num> +#> <chr> <dbl> #> 1 lp__ -5.00 #> 2 theta 0.2 +# Run 'optimize' again with 'jacobian=TRUE' and then draw from laplace approximation +# to the posterior +fit_optim <- mod$optimize(data = my_data_file, jacobian = TRUE) +#> Initial log joint probability = -6.79903 +#> Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes +#> 3 -6.74802 0.00379849 4.77236e-05 0.9516 0.9516 6 +#> Optimization terminated normally: +#> Convergence detected: relative gradient magnitude is below tolerance +#> Finished in 0.1 seconds. +fit_laplace <- mod$laplace(data = my_data_file, mode = fit_optim, draws = 2000) +#> Calculating Hessian +#> Calculating inverse of Cholesky factor +#> Generating draws +#> iteration: 0 +#> iteration: 100 +#> iteration: 200 +#> iteration: 300 +#> iteration: 400 +#> iteration: 500 +#> iteration: 600 +#> iteration: 700 +#> iteration: 800 +#> iteration: 900 +#> iteration: 1000 +#> iteration: 1100 +#> iteration: 1200 +#> iteration: 1300 +#> iteration: 1400 +#> iteration: 1500 +#> iteration: 1600 +#> iteration: 1700 +#> iteration: 1800 +#> iteration: 1900 +#> Finished in 0.1 seconds. +fit_laplace$summary() +#> # A tibble: 3 × 7 +#> variable mean median sd mad q5 q95 +#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 lp__ -7.22 -6.97 0.659 0.305 -8.52 -6.75 +#> 2 lp_approx__ -0.481 -0.224 0.662 0.308 -1.84 -0.00186 +#> 3 theta 0.272 0.252 0.121 0.122 0.103 0.502 -# Run 'variational' method to approximate the posterior (default is meanfield ADVI) +# Run 'variational' method to use ADVI to approximate posterior fit_vb <- mod$variational(data = stan_data, seed = 123) #> ------------------------------------------------------------ #> EXPERIMENTAL ALGORITHM: @@ -416,20 +425,47 @@

    Examples

    #> Drawing a sample of size 1000 from the approximate posterior... #> COMPLETED. #> Finished in 0.1 seconds. - fit_vb$summary() #> # A tibble: 3 × 7 #> variable mean median sd mad q5 q95 -#> <chr> <num> <num> <num> <num> <num> <num> +#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 lp__ -7.18 -6.94 0.588 0.259 -8.36 -6.75 #> 2 lp_approx__ -0.515 -0.221 0.692 0.303 -2.06 -0.00257 #> 3 theta 0.263 0.246 0.115 0.113 0.106 0.481 - -# Plot approximate posterior using bayesplot mcmc_hist(fit_vb$draws("theta")) #> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`. +# Run 'pathfinder' method, a new alternative to the variational method +fit_pf <- mod$pathfinder(data = stan_data, seed = 123) +#> Path [1] :Initial log joint density = -11.008832 +#> Path [1] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes +#> 5 -6.748e+00 9.383e-04 1.391e-05 1.000e+00 1.000e+00 126 -6.264e+00 -6.264e+00 +#> Path [1] :Best Iter: [3] ELBO (-6.195408) evaluations: (126) +#> Path [2] :Initial log joint density = -7.318450 +#> Path [2] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes +#> 4 -6.748e+00 5.414e-03 1.618e-04 1.000e+00 1.000e+00 101 -6.251e+00 -6.251e+00 +#> Path [2] :Best Iter: [3] ELBO (-6.229174) evaluations: (101) +#> Path [3] :Initial log joint density = -12.374612 +#> Path [3] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes +#> 5 -6.748e+00 1.419e-03 2.837e-05 1.000e+00 1.000e+00 126 -6.199e+00 -6.199e+00 +#> Path [3] :Best Iter: [5] ELBO (-6.199185) evaluations: (126) +#> Path [4] :Initial log joint density = -13.009824 +#> Path [4] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes +#> 5 -6.748e+00 1.677e-03 3.885e-05 1.000e+00 1.000e+00 126 -6.173e+00 -6.173e+00 +#> Path [4] :Best Iter: [5] ELBO (-6.172860) evaluations: (126) +#> Total log probability function evaluations:4379 +#> Finished in 0.1 seconds. +fit_pf$summary() +#> # A tibble: 3 × 7 +#> variable mean median sd mad q5 q95 +#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 lp_approx__ -1.08 -0.728 0.886 0.304 -2.71 -0.511 +#> 2 lp__ -7.26 -6.96 0.738 0.297 -8.72 -6.75 +#> 3 theta 0.249 0.230 0.120 0.121 0.0854 0.471 +mcmc_hist(fit_pf$draws("theta")) +#> `stat_bin()` using `bins = 30`. 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z+CH)3oVV2v@W0M61iIrru?5hm4JJ;1EQdYa4y%%ng9tHloD;s|BewU6p5nhyuqbIs X-_>q8e4k)E@TVZFDwA>X#{K^X0-4kq diff --git a/docs/reference/cmdstanr-package.html b/docs/reference/cmdstanr-package.html index 5cd82a34..694d6a38 100644 --- a/docs/reference/cmdstanr-package.html +++ b/docs/reference/cmdstanr-package.html @@ -19,7 +19,7 @@ cmdstanr - 0.6.0 + 0.7.0 @@ -189,13 +189,12 @@

    Examples

    # Otherwise use the `path` argument to specify the location of your # CmdStan installation.) set_cmdstan_path(path = NULL) -#> CmdStan path set to: /Users/jgabry/.cmdstan/cmdstan-2.32.2 +#> CmdStan path set to: /Users/jgabry/.cmdstan/cmdstan-2.33.1 # Create a CmdStanModel object from a Stan program, # here using the example model that comes with CmdStan file <- file.path(cmdstan_path(), "examples/bernoulli/bernoulli.stan") mod <- cmdstan_model(file) -#> Model executable is up to date! mod$print() #> data { #> int<lower=0> N; @@ -277,10 +276,22 @@

    Examples

    fit_mcmc$summary() #> # A tibble: 2 × 10 #> variable mean median sd mad q5 q95 rhat ess_bulk ess_tail -#> <chr> <num> <num> <num> <num> <num> <num> <num> <num> <num> +#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 lp__ -7.30 -7.03 0.721 0.380 -8.82 -6.75 1.00 902. 1006. #> 2 theta 0.247 0.233 0.122 0.129 0.0786 0.470 1.00 762. 712. +# Check sampling diagnostics +fit_mcmc$diagnostic_summary() +#> $num_divergent +#> [1] 0 0 +#> +#> $num_max_treedepth +#> [1] 0 0 +#> +#> $ebfmi +#> [1] 1.017555 1.250490 +#> + # Get posterior draws draws <- fit_mcmc$draws() print(draws) @@ -329,51 +340,8 @@

    Examples

    #> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`. -# Call CmdStan's diagnose and stansummary utilities -fit_mcmc$cmdstan_diagnose() -#> Processing csv files: /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/Rtmp9Z3PNu/bernoulli-202307271729-1-67e85d.csv, /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/Rtmp9Z3PNu/bernoulli-202307271729-2-67e85d.csv -#> -#> Checking sampler transitions treedepth. -#> Treedepth satisfactory for all transitions. -#> -#> Checking sampler transitions for divergences. -#> No divergent transitions found. -#> -#> Checking E-BFMI - sampler transitions HMC potential energy. -#> E-BFMI satisfactory. -#> -#> Effective sample size satisfactory. -#> -#> Split R-hat values satisfactory all parameters. -#> -#> Processing complete, no problems detected. -fit_mcmc$cmdstan_summary() -#> Inference for Stan model: bernoulli_model -#> 2 chains: each with iter=(1000,1000); warmup=(0,0); thin=(1,1); 2000 iterations saved. -#> -#> Warmup took (0.0040, 0.0030) seconds, 0.0070 seconds total -#> Sampling took (0.011, 0.011) seconds, 0.022 seconds total -#> -#> Mean MCSE StdDev 5% 50% 95% N_Eff N_Eff/s R_hat -#> -#> lp__ -7.3 2.6e-02 0.72 -8.8 -7.0 -6.8 781 35502 1.0 -#> accept_stat__ 0.92 8.3e-03 0.13 0.64 0.97 1.0 235 10662 1.0e+00 -#> stepsize__ 0.95 7.9e-02 0.079 0.87 1.0 1.0 1.0 46 2.0e+13 -#> treedepth__ 1.4 1.1e-02 0.48 1.0 1.0 2.0 1874 85179 1.0e+00 -#> n_leapfrog__ 2.5 1.4e-01 1.3 1.0 3.0 3.0 89 4050 1.0e+00 -#> divergent__ 0.00 nan 0.00 0.00 0.00 0.00 nan nan nan -#> energy__ 7.8 3.6e-02 1.00 6.8 7.5 9.6 775 35215 1.0e+00 -#> -#> theta 0.25 4.3e-03 0.12 0.079 0.23 0.47 796 36197 1.0 -#> -#> Samples were drawn using hmc with nuts. -#> For each parameter, N_Eff is a crude measure of effective sample size, -#> and R_hat is the potential scale reduction factor on split chains (at -#> convergence, R_hat=1). - # For models fit using MCMC, if you like working with RStan's stanfit objects # then you can create one with rstan::read_stan_csv() - # stanfit <- rstan::read_stan_csv(fit_mcmc$output_files()) @@ -387,24 +355,64 @@

    Examples

    #> Optimization terminated normally: #> Convergence detected: relative gradient magnitude is below tolerance #> Finished in 0.1 seconds. - fit_optim$summary() #> # A tibble: 2 × 2 #> variable estimate -#> <chr> <num> +#> <chr> <dbl> #> 1 lp__ -5.00 #> 2 theta 0.2 +# Run 'optimize' again with 'jacobian=TRUE' and then draw from laplace approximation +# to the posterior +fit_optim <- mod$optimize(data = my_data_file, jacobian = TRUE) +#> Initial log joint probability = -16.0056 +#> Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes +#> 5 -6.74802 0.00186556 5.60208e-05 1 1 8 +#> Optimization terminated normally: +#> Convergence detected: relative gradient magnitude is below tolerance +#> Finished in 0.1 seconds. +fit_laplace <- mod$laplace(data = my_data_file, mode = fit_optim, draws = 2000) +#> Calculating Hessian +#> Calculating inverse of Cholesky factor +#> Generating draws +#> iteration: 0 +#> iteration: 100 +#> iteration: 200 +#> iteration: 300 +#> iteration: 400 +#> iteration: 500 +#> iteration: 600 +#> iteration: 700 +#> iteration: 800 +#> iteration: 900 +#> iteration: 1000 +#> iteration: 1100 +#> iteration: 1200 +#> iteration: 1300 +#> iteration: 1400 +#> iteration: 1500 +#> iteration: 1600 +#> iteration: 1700 +#> iteration: 1800 +#> iteration: 1900 +#> Finished in 0.1 seconds. +fit_laplace$summary() +#> # A tibble: 3 × 7 +#> variable mean median sd mad q5 q95 +#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 lp__ -7.24 -6.98 0.709 0.312 -8.60 -6.75 +#> 2 lp_approx__ -0.506 -0.238 0.711 0.320 -1.94 -0.00243 +#> 3 theta 0.265 0.245 0.123 0.121 0.0981 0.496 -# Run 'variational' method to approximate the posterior (default is meanfield ADVI) +# Run 'variational' method to use ADVI to approximate posterior fit_vb <- mod$variational(data = stan_data, seed = 123) #> ------------------------------------------------------------ #> EXPERIMENTAL ALGORITHM: #> This procedure has not been thoroughly tested and may be unstable #> or buggy. The interface is subject to change. #> ------------------------------------------------------------ -#> Gradient evaluation took 5e-06 seconds -#> 1000 transitions using 10 leapfrog steps per transition would take 0.05 seconds. +#> Gradient evaluation took 6e-06 seconds +#> 1000 transitions using 10 leapfrog steps per transition would take 0.06 seconds. #> Adjust your expectations accordingly! #> Begin eta adaptation. #> Iteration: 1 / 250 [ 0%] (Adaptation) @@ -421,20 +429,47 @@

    Examples

    #> Drawing a sample of size 1000 from the approximate posterior... #> COMPLETED. #> Finished in 0.1 seconds. - fit_vb$summary() #> # A tibble: 3 × 7 #> variable mean median sd mad q5 q95 -#> <chr> <num> <num> <num> <num> <num> <num> +#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 lp__ -7.18 -6.94 0.588 0.259 -8.36 -6.75 #> 2 lp_approx__ -0.515 -0.221 0.692 0.303 -2.06 -0.00257 #> 3 theta 0.263 0.246 0.115 0.113 0.106 0.481 - -# Plot approximate posterior using bayesplot mcmc_hist(fit_vb$draws("theta")) #> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`. +# Run 'pathfinder' method, a new alternative to the variational method +fit_pf <- mod$pathfinder(data = stan_data, seed = 123) +#> Path [1] :Initial log joint density = -11.008832 +#> Path [1] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes +#> 5 -6.748e+00 9.383e-04 1.391e-05 1.000e+00 1.000e+00 126 -6.264e+00 -6.264e+00 +#> Path [1] :Best Iter: [3] ELBO (-6.195408) evaluations: (126) +#> Path [2] :Initial log joint density = -7.318450 +#> Path [2] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes +#> 4 -6.748e+00 5.414e-03 1.618e-04 1.000e+00 1.000e+00 101 -6.251e+00 -6.251e+00 +#> Path [2] :Best Iter: [3] ELBO (-6.229174) evaluations: (101) +#> Path [3] :Initial log joint density = -12.374612 +#> Path [3] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes +#> 5 -6.748e+00 1.419e-03 2.837e-05 1.000e+00 1.000e+00 126 -6.199e+00 -6.199e+00 +#> Path [3] :Best Iter: [5] ELBO (-6.199185) evaluations: (126) +#> Path [4] :Initial log joint density = -13.009824 +#> Path [4] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes +#> 5 -6.748e+00 1.677e-03 3.885e-05 1.000e+00 1.000e+00 126 -6.173e+00 -6.173e+00 +#> Path [4] :Best Iter: [5] ELBO (-6.172860) evaluations: (126) +#> Total log probability function evaluations:4379 +#> Finished in 0.1 seconds. +fit_pf$summary() +#> # A tibble: 3 × 7 +#> variable mean median sd mad q5 q95 +#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 lp_approx__ -1.08 -0.728 0.886 0.304 -2.71 -0.511 +#> 2 lp__ -7.26 -6.96 0.738 0.297 -8.72 -6.75 +#> 3 theta 0.249 0.230 0.120 0.121 0.0854 0.471 +mcmc_hist(fit_pf$draws("theta")) +#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`. + # Specifying initial values as a function fit_mcmc_w_init_fun <- mod$sample( diff --git a/docs/reference/cmdstanr_example.html b/docs/reference/cmdstanr_example.html index 8bc2b49b..babf2ce0 100644 --- a/docs/reference/cmdstanr_example.html +++ b/docs/reference/cmdstanr_example.html @@ -17,7 +17,7 @@ cmdstanr - 0.6.0 + 0.7.0 @@ -110,7 +110,7 @@

    Fit models for use in examples

    cmdstanr_example(
   example = c("logistic", "schools", "schools_ncp"),
-  method = c("sample", "optimize", "variational", "diagnose"),
+  method = c("sample", "optimize", "laplace", "variational", "pathfinder", "diagnose"),
   ...,
   quiet = TRUE,
   force_recompile = getOption("cmdstanr_force_recompile", default = FALSE)
@@ -149,8 +149,7 @@ 
    Arguments
    
 
 
 
    force_recompile
    
-
    Passed to the model-method-compile
-method.
    
+
    Passed to the $compile() method.
    @@ -186,26 +185,24 @@

    Examples

    #> } #> } fit_logistic_mcmc <- cmdstanr_example("logistic", chains = 2) -#> Model executable is up to date! fit_logistic_mcmc$summary() #> # A tibble: 105 × 10 -#> variable mean median sd mad q5 q95 rhat ess_bulk -#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 lp__ -66.0 -65.7 1.46 1.25 -68.9 -64.3 1.00 786. -#> 2 alpha 0.376 0.373 0.215 0.210 0.0346 0.747 1.00 1698. -#> 3 beta[1] -0.673 -0.671 0.261 0.249 -1.11 -0.251 1.00 1889. -#> 4 beta[2] -0.271 -0.267 0.228 0.239 -0.656 0.0921 1.00 2052. -#> 5 beta[3] 0.677 0.668 0.264 0.264 0.261 1.13 1.00 2070. -#> 6 log_lik[1] -0.515 -0.510 0.0982 0.0971 -0.685 -0.366 1.00 1843. -#> 7 log_lik[2] -0.402 -0.381 0.147 0.141 -0.670 -0.196 1.00 1893. -#> 8 log_lik[3] -0.496 -0.456 0.221 0.205 -0.905 -0.212 1.00 1969. -#> 9 log_lik[4] -0.449 -0.434 0.148 0.153 -0.721 -0.233 1.00 1958. -#> 10 log_lik[5] -1.18 -1.16 0.273 0.267 -1.65 -0.760 1.00 1991. +#> variable mean median sd mad q5 q95 rhat ess_bulk +#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 lp__ -66.1 -65.7 1.53 1.26 -69.1 -64.3 1.00 1090. +#> 2 alpha 0.373 0.371 0.223 0.215 0.0169 0.729 1.00 1944. +#> 3 beta[1] -0.678 -0.676 0.260 0.261 -1.11 -0.265 1.00 1936. +#> 4 beta[2] -0.276 -0.274 0.234 0.239 -0.674 0.0978 1.00 1841. +#> 5 beta[3] 0.684 0.681 0.268 0.270 0.270 1.13 1.00 2070. +#> 6 log_lik[1] -0.518 -0.509 0.102 0.101 -0.694 -0.366 0.999 1983. +#> 7 log_lik[2] -0.398 -0.380 0.149 0.143 -0.665 -0.194 1.00 2108. +#> 8 log_lik[3] -0.501 -0.461 0.228 0.210 -0.939 -0.204 1.00 1995. +#> 9 log_lik[4] -0.446 -0.428 0.157 0.148 -0.740 -0.226 1.00 1806. +#> 10 log_lik[5] -1.18 -1.15 0.285 0.286 -1.67 -0.754 1.00 2253. #> # ℹ 95 more rows #> # ℹ 1 more variable: ess_tail <dbl> fit_logistic_optim <- cmdstanr_example("logistic", method = "optimize") -#> Model executable is up to date! fit_logistic_optim$summary() #> # A tibble: 105 × 2 #> variable estimate @@ -214,7 +211,7 @@

    Examples

    #> 2 alpha 0.364 #> 3 beta[1] -0.632 #> 4 beta[2] -0.259 -#> 5 beta[3] 0.648 +#> 5 beta[3] 0.649 #> 6 log_lik[1] -0.515 #> 7 log_lik[2] -0.394 #> 8 log_lik[3] -0.469 @@ -223,21 +220,20 @@

    Examples

    #> # ℹ 95 more rows fit_logistic_vb <- cmdstanr_example("logistic", method = "variational") -#> Model executable is up to date! fit_logistic_vb$summary() #> # A tibble: 106 × 7 -#> variable mean median sd mad q5 q95 -#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 lp__ -66.0 -65.6 1.51 1.24 -69.1 -64.3 -#> 2 lp_approx__ -2.01 -1.67 1.46 1.19 -4.72 -0.370 -#> 3 alpha 0.343 0.336 0.198 0.203 0.0276 0.671 -#> 4 beta[1] -0.664 -0.666 0.268 0.258 -1.08 -0.215 -#> 5 beta[2] -0.285 -0.281 0.193 0.194 -0.607 0.0295 -#> 6 beta[3] 0.720 0.706 0.285 0.282 0.277 1.23 -#> 7 log_lik[1] -0.534 -0.529 0.0945 0.0944 -0.703 -0.391 -#> 8 log_lik[2] -0.378 -0.365 0.147 0.143 -0.632 -0.172 -#> 9 log_lik[3] -0.508 -0.477 0.200 0.201 -0.879 -0.237 -#> 10 log_lik[4] -0.418 -0.405 0.131 0.132 -0.652 -0.232 +#> variable mean median sd mad q5 q95 +#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 lp__ -66.5 -66.1 1.98 1.74 -70.5 -64.4 +#> 2 lp_approx__ -2.01 -1.63 1.53 1.27 -5.07 -0.341 +#> 3 alpha 0.305 0.311 0.248 0.234 -0.116 0.716 +#> 4 beta[1] -0.623 -0.633 0.230 0.239 -0.987 -0.255 +#> 5 beta[2] -0.377 -0.369 0.263 0.258 -0.821 0.0339 +#> 6 beta[3] 0.681 0.671 0.291 0.288 0.215 1.16 +#> 7 log_lik[1] -0.565 -0.558 0.118 0.108 -0.766 -0.383 +#> 8 log_lik[2] -0.412 -0.388 0.172 0.156 -0.740 -0.186 +#> 9 log_lik[3] -0.641 -0.589 0.294 0.280 -1.22 -0.265 +#> 10 log_lik[4] -0.389 -0.367 0.145 0.134 -0.662 -0.190 #> # ℹ 96 more rows print_example_program("schools") @@ -258,24 +254,23 @@

    Examples

    #> target += normal_lpdf(y | theta, sigma); #> } fit_schools_mcmc <- cmdstanr_example("schools") -#> Model executable is up to date! -#> Warning: 211 of 4000 (5.0%) transitions ended with a divergence. +#> Warning: 91 of 4000 (2.0%) transitions ended with a divergence. #> See https://mc-stan.org/misc/warnings for details. fit_schools_mcmc$summary() #> # A tibble: 11 × 10 #> variable mean median sd mad q5 q95 rhat ess_bulk ess_tail #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 lp__ -57.8 -58.4 5.90 5.92 -66.5 -46.9 1.03 134. 91.1 -#> 2 mu 6.59 6.58 4.19 4.02 -0.239 13.4 1.01 605. 759. -#> 3 tau 5.32 4.54 3.72 3.56 0.991 12.7 1.02 129. 68.3 -#> 4 theta[1] 9.40 8.66 6.82 5.74 -0.443 21.5 1.01 543. 1393. -#> 5 theta[2] 6.95 6.63 5.60 5.01 -2.06 15.9 1.01 912. 1777. -#> 6 theta[3] 5.44 5.77 6.76 5.78 -5.92 15.4 1.01 1075. 2014. -#> 7 theta[4] 6.74 6.67 5.84 5.21 -3.08 16.3 1.01 1107. 1798. -#> 8 theta[5] 4.67 4.90 5.73 5.26 -5.35 13.2 1.02 853. 1100. -#> 9 theta[6] 5.53 5.51 6.11 5.41 -5.04 15.0 1.01 1068. 1575. -#> 10 theta[7] 9.19 8.53 6.04 5.70 0.489 20.1 1.01 474. 1994. -#> 11 theta[8] 7.09 7.01 6.55 5.52 -3.34 17.8 1.02 820. 1987. +#> 1 lp__ -57.8 -58.2 5.50 5.71 -66.3 -48.3 1.04 151. 248. +#> 2 mu 6.47 6.50 4.26 4.17 -1.05 13.3 1.01 464. 141. +#> 3 tau 5.22 4.49 3.59 3.40 0.984 12.2 1.04 115. 122. +#> 4 theta[1] 9.21 8.56 7.05 6.14 -1.40 21.8 1.01 427. 239. +#> 5 theta[2] 6.77 6.75 5.62 5.20 -2.79 15.9 1.01 718. 443. +#> 6 theta[3] 5.35 5.83 6.82 5.87 -6.13 15.5 1.01 1103. 1908. +#> 7 theta[4] 6.84 6.85 5.86 5.44 -2.42 16.6 1.01 923. 2076. +#> 8 theta[5] 4.64 4.91 5.62 5.40 -4.88 13.4 1.01 775. 1603. +#> 9 theta[6] 5.51 5.70 6.05 5.48 -4.39 15.1 1.01 639. 1878. +#> 10 theta[7] 8.94 8.42 6.14 5.47 -0.805 19.6 1.02 272. 164. +#> 11 theta[8] 7.08 6.94 6.75 5.70 -3.52 18.0 1.01 969. 1764. print_example_program("schools_ncp") #> data { @@ -298,55 +293,42 @@

    Examples

    #> target += normal_lpdf(y | theta, sigma); #> } fit_schools_ncp_mcmc <- cmdstanr_example("schools_ncp") -#> Model executable is up to date! fit_schools_ncp_mcmc$summary() #> # A tibble: 19 × 10 -#> variable mean median sd mad q5 q95 rhat ess_bulk ess_tail -#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 lp__ -46.9 -4.66e+1 2.45 2.38 -51.4 -43.4 1.00 1684. 2222. -#> 2 mu 6.53 6.60e+0 4.23 4.29 -0.526 13.3 1.00 2821. 2399. -#> 3 tau 4.78 4.00e+0 3.65 3.45 0.323 12.0 1.00 2114. 2038. -#> 4 theta_r… 0.379 4.09e-1 0.987 0.963 -1.27 1.95 1.00 3181. 2648. -#> 5 theta_r… 0.0512 7.13e-2 0.909 0.893 -1.45 1.56 1.00 3571. 2470. -#> 6 theta_r… -0.155 -1.61e-1 0.953 0.950 -1.71 1.40 1.00 3651. 2384. -#> 7 theta_r… 0.0143 9.01e-3 0.888 0.872 -1.39 1.49 1.00 3590. 2857. -#> 8 theta_r… -0.266 -2.77e-1 0.898 0.888 -1.71 1.23 1.00 3506. 2698. -#> 9 theta_r… -0.144 -1.44e-1 0.924 0.895 -1.66 1.36 1.00 3308. 2961. -#> 10 theta_r… 0.361 3.56e-1 0.954 0.946 -1.25 1.89 1.00 3656. 2540. -#> 11 theta_r… 0.0480 3.80e-2 0.959 0.953 -1.52 1.68 1.00 4024. 2776. -#> 12 theta[1] 9.07 8.48e+0 6.90 5.95 -0.696 21.7 1.00 3565. 2983. -#> 13 theta[2] 6.86 6.81e+0 5.56 5.13 -2.16 15.9 1.00 4325. 2944. -#> 14 theta[3] 5.45 5.83e+0 6.39 5.75 -5.69 15.0 1.00 3605. 3177. -#> 15 theta[4] 6.67 6.67e+0 5.72 5.16 -2.73 16.2 0.999 4154. 3160. -#> 16 theta[5] 4.93 5.18e+0 5.62 5.36 -4.69 13.4 1.00 3410. 2956. -#> 17 theta[6] 5.62 5.85e+0 5.82 5.33 -4.24 14.9 1.00 3805. 3138. -#> 18 theta[7] 8.96 8.44e+0 6.20 5.60 -0.266 20.2 1.00 3687. 3063. -#> 19 theta[8] 6.92 6.75e+0 6.59 5.75 -3.69 17.7 1.00 3285. 3072. +#> variable mean median sd mad q5 q95 rhat ess_bulk +#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 lp__ -46.9 -46.6 2.47 2.39 -51.5 -43.5 1.00 1443. +#> 2 mu 6.50 6.57 4.25 4.13 -0.446 13.4 1.00 2941. +#> 3 tau 4.78 3.91 3.74 3.46 0.374 12.1 1.00 1743. +#> 4 theta_raw[1] 0.372 0.381 0.989 0.951 -1.28 1.97 1.00 3537. +#> 5 theta_raw[2] 0.0565 0.0620 0.915 0.928 -1.45 1.53 1.00 4450. +#> 6 theta_raw[3] -0.148 -0.163 0.938 0.932 -1.65 1.40 1.00 3407. +#> 7 theta_raw[4] 0.00702 0.00718 0.937 0.912 -1.55 1.53 1.00 3544. +#> 8 theta_raw[5] -0.264 -0.271 0.896 0.870 -1.70 1.23 1.00 3495. +#> 9 theta_raw[6] -0.148 -0.157 0.950 0.938 -1.68 1.43 1.00 4517. +#> 10 theta_raw[7] 0.351 0.374 0.924 0.876 -1.23 1.82 1.00 3596. +#> 11 theta_raw[8] 0.0685 0.0540 0.960 0.970 -1.52 1.64 1.00 3657. +#> 12 theta[1] 8.96 8.30 6.79 5.56 -0.975 21.5 1.00 3374. +#> 13 theta[2] 6.90 6.72 5.56 5.25 -2.07 16.0 1.00 4663. +#> 14 theta[3] 5.58 5.91 6.35 5.42 -5.10 15.2 1.00 3392. +#> 15 theta[4] 6.67 6.72 6.02 5.40 -2.96 16.2 1.00 3971. +#> 16 theta[5] 4.81 5.19 5.50 5.21 -4.81 12.9 1.00 4119. +#> 17 theta[6] 5.55 5.86 5.93 5.29 -4.38 14.7 1.00 4010. +#> 18 theta[7] 8.73 8.24 5.88 5.28 -0.0503 19.3 1.00 3955. +#> 19 theta[8] 7.01 6.84 6.39 5.49 -3.28 17.9 1.00 3861. +#> # ℹ 1 more variable: ess_tail <dbl> # optimization fails for hierarchical model cmdstanr_example("schools", "optimize", quiet = FALSE) -#> Model executable is up to date! -#> Initial log joint probability = -74.0754 +#> Initial log joint probability = -319.937 #> Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes -#> 99 114.137 0.279807 1.66573e+09 0.5386 0.1135 171 +#> 99 120.913 0.248985 3.76333e+09 1 1 183 #> Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes -#> 189 249.295 0.0236622 1.57992e+16 1e-12 0.001 399 LS failed, Hessian reset -#> Optimization terminated with error: -#> Line search failed to achieve a sufficient decrease, no more progress can be made -#> Finished in 0.1 seconds. -#> variable estimate -#> lp__ 249.29 -#> mu 0.95 -#> tau 0.00 -#> theta[1] 0.95 -#> theta[2] 0.95 -#> theta[3] 0.95 -#> theta[4] 0.95 -#> theta[5] 0.95 -#> theta[6] 0.95 -#> theta[7] 0.95 -#> -#> # showing 10 of 11 rows (change via 'max_rows' argument or 'cmdstanr_max_rows' option) +#> 180 242.851 0.0892321 2.24452e+16 1e-12 0.001 378 LS failed, Hessian reset +#> Line search failed to achieve a sufficient decrease, no more progress can be made +#> Chain 1 Optimization terminated with error: +#> Warning: Fitting finished unexpectedly! Use the $output() method for more information. +#> Error: Fitting failed. Unable to print. # }
    diff --git a/docs/reference/draws_to_csv.html b/docs/reference/draws_to_csv.html index 08c28b5e..9f523602 100644 --- a/docs/reference/draws_to_csv.html +++ b/docs/reference/draws_to_csv.html @@ -20,7 +20,7 @@ cmdstanr - 0.6.0 + 0.7.0
    @@ -169,10 +169,10 @@

    Examples

    draws_csv_files <- draws_to_csv(draws) print(draws_csv_files) -#> [1] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T//RtmpFBtN6X/fittedParams-202307251435-1-8744f9.csv" -#> [2] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T//RtmpFBtN6X/fittedParams-202307251435-2-8744f9.csv" -#> [3] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T//RtmpFBtN6X/fittedParams-202307251435-3-8744f9.csv" -#> [4] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T//RtmpFBtN6X/fittedParams-202307251435-4-8744f9.csv" +#> [1] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T//RtmpGCiVFY/fittedParams-202311081026-1-7ea6a2.csv" +#> [2] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T//RtmpGCiVFY/fittedParams-202311081026-2-7ea6a2.csv" +#> [3] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T//RtmpGCiVFY/fittedParams-202311081026-3-7ea6a2.csv" +#> [4] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T//RtmpGCiVFY/fittedParams-202311081026-4-7ea6a2.csv" # draws_csv_files <- draws_to_csv(draws, # sampler_diagnostic = sampler_diagnostics, diff --git a/docs/reference/eng_cmdstan.html b/docs/reference/eng_cmdstan.html index f39ce5ce..3430c9d7 100644 --- a/docs/reference/eng_cmdstan.html +++ b/docs/reference/eng_cmdstan.html @@ -22,7 +22,7 @@ cmdstanr - 0.6.0 + 0.7.0 diff --git a/docs/reference/fit-method-cmdstan_summary.html b/docs/reference/fit-method-cmdstan_summary.html index 7aaf6716..2ab3dc62 100644 --- a/docs/reference/fit-method-cmdstan_summary.html +++ b/docs/reference/fit-method-cmdstan_summary.html @@ -28,7 +28,7 @@ cmdstanr - 0.6.0 + 0.7.0 @@ -149,7 +149,7 @@

    Examples

    # \dontrun{
 fit <- cmdstanr_example("logistic")
 fit$cmdstan_diagnose()
-#> Processing csv files: /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpFBtN6X/logistic-202307251435-1-7b6c9f.csv, /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpFBtN6X/logistic-202307251435-2-7b6c9f.csv, /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpFBtN6X/logistic-202307251435-3-7b6c9f.csv, /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpFBtN6X/logistic-202307251435-4-7b6c9f.csv
+#> Processing csv files: /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-202311081026-1-1f791d.csv, /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-202311081026-2-1f791d.csv, /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-202311081026-3-1f791d.csv, /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-202311081026-4-1f791d.csv
 #> 
 #> Checking sampler transitions treedepth.
 #> Treedepth satisfactory for all transitions.
@@ -169,123 +169,123 @@ 
    Examples
    
 #> Inference for Stan model: logistic_model
 #> 4 chains: each with iter=(1000,1000,1000,1000); warmup=(0,0,0,0); thin=(1,1,1,1); 4000 iterations saved.
 #> 
-#> Warmup took (0.022, 0.022, 0.022, 0.021) seconds, 0.087 seconds total
-#> Sampling took (0.069, 0.072, 0.068, 0.066) seconds, 0.28 seconds total
+#> Warmup took (0.023, 0.023, 0.023, 0.023) seconds, 0.092 seconds total
+#> Sampling took (0.076, 0.070, 0.072, 0.073) seconds, 0.29 seconds total
 #> 
 #>                     Mean     MCSE  StdDev     5%       50%       95%  N_Eff  N_Eff/s    R_hat
 #> 
-#> lp__            -6.6e+01  3.3e-02     1.4    -69  -6.6e+01  -6.4e+01   1875     6818      1.0
-#> accept_stat__       0.91  1.3e-03   0.099   0.71      0.95       1.0   5487    19952  1.0e+00
-#> stepsize__          0.75  2.8e-02   0.040   0.69      0.76      0.80    2.0      7.3  1.2e+13
-#> treedepth__          2.4  9.4e-03    0.53    2.0       2.0       3.0   3168    11520  1.0e+00
-#> n_leapfrog__         5.3  3.3e-02     2.0    3.0       7.0       7.0   3698    13446  1.0e+00
+#> lp__            -6.6e+01  3.0e-02     1.4    -69  -6.6e+01  -6.4e+01   2277     7826      1.0
+#> accept_stat__       0.89  1.8e-03    0.12   0.63      0.94       1.0   4996    17168  1.0e+00
+#> stepsize__          0.80  4.6e-02   0.066   0.73      0.85      0.88    2.0      6.9  2.6e+13
+#> treedepth__          2.3  8.8e-02    0.50    2.0       2.0       3.0     32      109  1.0e+00
+#> n_leapfrog__         4.8  2.8e-01     2.0    3.0       3.0       7.0     53      182  1.0e+00
 #> divergent__         0.00      nan    0.00   0.00      0.00      0.00    nan      nan      nan
-#> energy__              68  5.2e-02     2.0     65        68        72   1501     5457  1.0e+00
+#> energy__              68  4.5e-02     2.0     65        68        72   1941     6671  1.0e+00
 #> 
-#> alpha            3.8e-01  3.3e-03    0.22  0.031   3.8e-01   7.3e-01   4281    15567     1.00
-#> beta[1]         -6.6e-01  3.9e-03    0.25   -1.1  -6.6e-01  -2.5e-01   4206    15294     1.00
-#> beta[2]         -2.8e-01  3.7e-03    0.23  -0.65  -2.8e-01   1.0e-01   3812    13860      1.0
-#> beta[3]          6.8e-01  4.4e-03    0.27   0.25   6.8e-01   1.1e+00   3764    13688     1.00
-#> log_lik[1]      -5.2e-01  1.5e-03   0.097  -0.69  -5.1e-01  -3.7e-01   4137    15045      1.0
-#> log_lik[2]      -4.0e-01  2.4e-03    0.15  -0.69  -3.8e-01  -2.0e-01   4070    14798     1.00
-#> log_lik[3]      -5.0e-01  3.5e-03    0.22  -0.90  -4.7e-01  -2.1e-01   3974    14450      1.0
-#> log_lik[4]      -4.5e-01  2.4e-03    0.15  -0.72  -4.3e-01  -2.4e-01   3841    13967     1.00
-#> log_lik[5]      -1.2e+00  4.3e-03    0.28   -1.7  -1.2e+00  -7.6e-01   4262    15497     1.00
-#> log_lik[6]      -5.9e-01  2.8e-03    0.19  -0.94  -5.7e-01  -3.1e-01   4639    16869     1.00
-#> log_lik[7]      -6.4e-01  1.8e-03    0.13  -0.86  -6.3e-01  -4.5e-01   4605    16746     1.00
-#> log_lik[8]      -2.8e-01  2.3e-03    0.14  -0.54  -2.5e-01  -1.1e-01   3527    12826      1.0
-#> log_lik[9]      -6.9e-01  2.6e-03    0.17  -0.99  -6.8e-01  -4.3e-01   4522    16442     1.00
-#> log_lik[10]     -7.4e-01  3.7e-03    0.23   -1.2  -7.2e-01  -4.1e-01   3966    14423      1.0
-#> log_lik[11]     -2.8e-01  2.1e-03    0.13  -0.52  -2.6e-01  -1.1e-01   3682    13390      1.0
-#> log_lik[12]     -5.0e-01  3.9e-03    0.24  -0.93  -4.5e-01  -1.9e-01   3842    13973     1.00
-#> log_lik[13]     -6.6e-01  3.1e-03    0.21   -1.0  -6.4e-01  -3.6e-01   4585    16673     1.00
-#> log_lik[14]     -3.7e-01  2.8e-03    0.17  -0.70  -3.4e-01  -1.4e-01   3858    14029      1.0
-#> log_lik[15]     -2.8e-01  1.7e-03    0.11  -0.48  -2.7e-01  -1.4e-01   3977    14463      1.0
-#> log_lik[16]     -2.8e-01  1.5e-03   0.087  -0.44  -2.6e-01  -1.5e-01   3495    12708      1.0
-#> log_lik[17]     -1.6e+00  5.1e-03    0.29   -2.1  -1.6e+00  -1.1e+00   3184    11579      1.0
-#> log_lik[18]     -4.8e-01  1.7e-03    0.11  -0.68  -4.7e-01  -3.1e-01   4019    14615      1.0
-#> log_lik[19]     -2.3e-01  1.3e-03   0.075  -0.37  -2.2e-01  -1.3e-01   3432    12479      1.0
-#> log_lik[20]     -1.1e-01  1.3e-03   0.080  -0.27  -9.4e-02  -2.9e-02   3626    13186      1.0
-#> log_lik[21]     -2.1e-01  1.5e-03   0.088  -0.38  -2.0e-01  -9.4e-02   3506    12749      1.0
-#> log_lik[22]     -5.7e-01  2.3e-03    0.15  -0.84  -5.5e-01  -3.4e-01   4293    15611      1.0
-#> log_lik[23]     -3.3e-01  2.4e-03    0.14  -0.60  -3.0e-01  -1.4e-01   3628    13194      1.0
-#> log_lik[24]     -1.4e-01  1.1e-03   0.066  -0.26  -1.2e-01  -5.2e-02   3504    12742      1.0
-#> log_lik[25]     -4.5e-01  1.8e-03    0.12  -0.67  -4.4e-01  -2.8e-01   4234    15396     1.00
-#> log_lik[26]     -1.5e+00  5.5e-03    0.34   -2.1  -1.5e+00  -1.0e+00   3760    13672      1.0
-#> log_lik[27]     -3.1e-01  2.0e-03    0.12  -0.54  -2.9e-01  -1.4e-01   3688    13412      1.0
-#> log_lik[28]     -4.5e-01  1.3e-03   0.083  -0.59  -4.4e-01  -3.2e-01   4007    14572      1.0
-#> log_lik[29]     -7.2e-01  3.4e-03    0.22   -1.1  -7.0e-01  -4.0e-01   4367    15880     1.00
-#> log_lik[30]     -6.9e-01  3.1e-03    0.20   -1.0  -6.7e-01  -4.1e-01   4146    15077      1.0
-#> log_lik[31]     -4.9e-01  2.5e-03    0.17  -0.80  -4.6e-01  -2.5e-01   4260    15491      1.0
-#> log_lik[32]     -4.2e-01  1.6e-03    0.11  -0.61  -4.2e-01  -2.7e-01   4253    15465     1.00
-#> log_lik[33]     -4.1e-01  2.0e-03    0.13  -0.65  -4.0e-01  -2.3e-01   4207    15297     1.00
-#> log_lik[34]     -6.3e-02  8.6e-04   0.051  -0.16  -4.9e-02  -1.3e-02   3530    12835     1.00
-#> log_lik[35]     -5.9e-01  2.6e-03    0.18  -0.91  -5.7e-01  -3.2e-01   4846    17624     1.00
-#> log_lik[36]     -3.2e-01  2.2e-03    0.13  -0.57  -3.0e-01  -1.5e-01   3788    13775      1.0
-#> log_lik[37]     -7.0e-01  3.4e-03    0.22   -1.1  -6.8e-01  -3.8e-01   4249    15451      1.0
-#> log_lik[38]     -3.1e-01  2.4e-03    0.15  -0.60  -2.8e-01  -1.2e-01   3822    13898     1.00
-#> log_lik[39]     -1.8e-01  1.8e-03    0.11  -0.40  -1.6e-01  -5.3e-02   3734    13579      1.0
-#> log_lik[40]     -6.8e-01  2.0e-03    0.13  -0.91  -6.7e-01  -4.8e-01   4407    16026     1.00
-#> log_lik[41]     -1.1e+00  4.2e-03    0.26   -1.6  -1.1e+00  -7.4e-01   3931    14294      1.0
-#> log_lik[42]     -9.3e-01  3.1e-03    0.20   -1.3  -9.1e-01  -6.2e-01   4217    15335     1.00
-#> log_lik[43]     -4.1e-01  4.0e-03    0.27  -0.92  -3.5e-01  -1.0e-01   4495    16345     1.00
-#> log_lik[44]     -1.2e+00  2.9e-03    0.18   -1.5  -1.2e+00  -8.9e-01   3801    13820      1.0
-#> log_lik[45]     -3.6e-01  1.9e-03    0.12  -0.58  -3.5e-01  -1.9e-01   3946    14348      1.0
-#> log_lik[46]     -5.8e-01  2.0e-03    0.13  -0.81  -5.7e-01  -3.8e-01   4290    15599     1.00
-#> log_lik[47]     -3.0e-01  2.1e-03    0.13  -0.55  -2.8e-01  -1.3e-01   3827    13916     1.00
-#> log_lik[48]     -3.2e-01  1.3e-03   0.082  -0.47  -3.2e-01  -2.0e-01   3733    13576      1.0
-#> log_lik[49]     -3.2e-01  1.3e-03   0.079  -0.46  -3.1e-01  -2.0e-01   3527    12825      1.0
-#> log_lik[50]     -1.3e+00  5.1e-03    0.33   -1.9  -1.3e+00  -8.0e-01   4204    15288     1.00
-#> log_lik[51]     -2.9e-01  1.5e-03   0.094  -0.46  -2.8e-01  -1.6e-01   4007    14571     1.00
-#> log_lik[52]     -8.3e-01  2.2e-03    0.14   -1.1  -8.2e-01  -6.2e-01   4159    15122     1.00
-#> log_lik[53]     -4.0e-01  2.1e-03    0.13  -0.65  -3.8e-01  -2.1e-01   4104    14922      1.0
-#> log_lik[54]     -3.7e-01  2.2e-03    0.14  -0.63  -3.6e-01  -1.8e-01   4025    14635      1.0
-#> log_lik[55]     -3.9e-01  2.2e-03    0.14  -0.64  -3.7e-01  -2.0e-01   4074    14813     1.00
-#> log_lik[56]     -3.2e-01  3.2e-03    0.19  -0.70  -2.7e-01  -9.4e-02   3722    13536      1.0
-#> log_lik[57]     -6.6e-01  1.7e-03    0.12  -0.86  -6.5e-01  -4.8e-01   4340    15782     1.00
-#> log_lik[58]     -9.5e-01  5.2e-03    0.35   -1.6  -9.1e-01  -4.6e-01   4377    15915     1.00
-#> log_lik[59]     -1.4e+00  5.6e-03    0.35   -2.0  -1.3e+00  -8.4e-01   3916    14239     1.00
-#> log_lik[60]     -9.8e-01  2.4e-03    0.16   -1.2  -9.7e-01  -7.3e-01   4048    14720     1.00
-#> log_lik[61]     -5.4e-01  1.5e-03   0.097  -0.71  -5.3e-01  -3.9e-01   4267    15517     1.00
-#> log_lik[62]     -8.7e-01  4.9e-03    0.32   -1.5  -8.3e-01  -4.3e-01   4186    15223     1.00
-#> log_lik[63]     -1.2e-01  1.3e-03   0.074  -0.26  -9.7e-02  -3.2e-02   3420    12438      1.0
-#> log_lik[64]     -9.1e-01  4.0e-03    0.26   -1.4  -8.8e-01  -5.3e-01   4216    15333      1.0
-#> log_lik[65]     -2.0e+00  1.0e-02    0.60   -3.1  -2.0e+00  -1.1e+00   3486    12678     1.00
-#> log_lik[66]     -5.1e-01  2.0e-03    0.13  -0.75  -5.0e-01  -3.1e-01   4559    16578     1.00
-#> log_lik[67]     -2.8e-01  1.4e-03   0.081  -0.42  -2.7e-01  -1.6e-01   3503    12740      1.0
-#> log_lik[68]     -1.1e+00  3.9e-03    0.24   -1.5  -1.1e+00  -7.0e-01   3902    14189      1.0
-#> log_lik[69]     -4.4e-01  1.3e-03   0.082  -0.58  -4.3e-01  -3.1e-01   4099    14906      1.0
-#> log_lik[70]     -6.4e-01  3.8e-03    0.24   -1.1  -6.0e-01  -3.0e-01   4023    14629      1.0
-#> log_lik[71]     -6.1e-01  3.0e-03    0.21  -0.99  -5.9e-01  -3.2e-01   4800    17455     1.00
-#> log_lik[72]     -4.7e-01  2.7e-03    0.18  -0.80  -4.4e-01  -2.2e-01   4327    15734     1.00
-#> log_lik[73]     -1.5e+00  6.0e-03    0.37   -2.1  -1.5e+00  -9.4e-01   3679    13379     1.00
-#> log_lik[74]     -9.5e-01  3.0e-03    0.19   -1.3  -9.4e-01  -6.6e-01   4126    15004      1.0
-#> log_lik[75]     -1.1e+00  6.0e-03    0.39   -1.8  -1.1e+00  -5.8e-01   4255    15474     1.00
-#> log_lik[76]     -3.8e-01  2.2e-03    0.14  -0.63  -3.6e-01  -1.8e-01   3886    14131      1.0
-#> log_lik[77]     -8.8e-01  2.1e-03    0.14   -1.1  -8.7e-01  -6.6e-01   4420    16075     1.00
-#> log_lik[78]     -4.9e-01  2.6e-03    0.17  -0.80  -4.7e-01  -2.5e-01   4490    16326     1.00
-#> log_lik[79]     -7.7e-01  2.9e-03    0.19   -1.1  -7.5e-01  -4.9e-01   4123    14992     1.00
-#> log_lik[80]     -5.4e-01  3.0e-03    0.20  -0.92  -5.2e-01  -2.6e-01   4287    15591     1.00
-#> log_lik[81]     -1.6e-01  1.6e-03   0.099  -0.36  -1.4e-01  -4.9e-02   3918    14248     1.00
-#> log_lik[82]     -2.3e-01  2.3e-03    0.14  -0.51  -1.9e-01  -6.4e-02   3867    14061     1.00
-#> log_lik[83]     -3.4e-01  1.3e-03   0.080  -0.49  -3.4e-01  -2.3e-01   3809    13853      1.0
-#> log_lik[84]     -2.8e-01  1.5e-03   0.090  -0.45  -2.7e-01  -1.5e-01   3815    13872      1.0
-#> log_lik[85]     -1.3e-01  1.3e-03   0.075  -0.28  -1.2e-01  -4.4e-02   3631    13202      1.0
-#> log_lik[86]     -1.1e+00  5.2e-03    0.33   -1.7  -1.1e+00  -6.4e-01   4016    14603     1.00
-#> log_lik[87]     -8.2e-01  1.9e-03    0.13   -1.0  -8.2e-01  -6.3e-01   4341    15786     1.00
-#> log_lik[88]     -7.8e-01  3.7e-03    0.25   -1.2  -7.5e-01  -4.3e-01   4546    16530     1.00
-#> log_lik[89]     -1.3e+00  5.2e-03    0.32   -1.8  -1.3e+00  -8.0e-01   3869    14070     1.00
-#> log_lik[90]     -2.6e-01  2.1e-03    0.13  -0.51  -2.3e-01  -9.4e-02   4086    14857     1.00
-#> log_lik[91]     -3.9e-01  2.0e-03    0.13  -0.63  -3.8e-01  -2.0e-01   4332    15753     1.00
-#> log_lik[92]     -1.5e+00  5.5e-03    0.34   -2.1  -1.5e+00  -9.7e-01   3795    13801      1.0
-#> log_lik[93]     -7.3e-01  3.5e-03    0.22   -1.1  -7.1e-01  -4.2e-01   3875    14091     1.00
-#> log_lik[94]     -3.2e-01  1.4e-03   0.086  -0.48  -3.1e-01  -1.9e-01   3797    13806      1.0
-#> log_lik[95]     -3.9e-01  1.9e-03    0.11  -0.60  -3.7e-01  -2.2e-01   3692    13426      1.0
-#> log_lik[96]     -1.6e+00  4.9e-03    0.28   -2.1  -1.6e+00  -1.1e+00   3247    11808      1.0
-#> log_lik[97]     -4.3e-01  1.6e-03    0.10  -0.61  -4.2e-01  -2.8e-01   4122    14989      1.0
-#> log_lik[98]     -1.1e+00  5.5e-03    0.37   -1.7  -1.0e+00  -5.3e-01   4388    15957     1.00
-#> log_lik[99]     -7.0e-01  2.2e-03    0.15  -0.95  -6.9e-01  -4.8e-01   4345    15799     1.00
-#> log_lik[100]    -3.9e-01  1.5e-03   0.096  -0.56  -3.8e-01  -2.5e-01   4119    14977      1.0
+#> alpha            3.8e-01  3.5e-03    0.22  0.014   3.8e-01   7.4e-01   4054    13931      1.0
+#> beta[1]         -6.7e-01  3.7e-03    0.25   -1.1  -6.7e-01  -2.8e-01   4566    15692      1.0
+#> beta[2]         -2.8e-01  3.9e-03    0.23  -0.66  -2.8e-01   9.5e-02   3521    12100      1.0
+#> beta[3]          6.8e-01  4.0e-03    0.26   0.25   6.8e-01   1.1e+00   4334    14894      1.0
+#> log_lik[1]      -5.2e-01  1.6e-03    0.10  -0.69  -5.1e-01  -3.6e-01   4009    13775      1.0
+#> log_lik[2]      -4.0e-01  2.2e-03    0.15  -0.69  -3.8e-01  -1.9e-01   4671    16052      1.0
+#> log_lik[3]      -5.0e-01  3.7e-03    0.22  -0.91  -4.7e-01  -2.1e-01   3570    12267      1.0
+#> log_lik[4]      -4.5e-01  2.4e-03    0.15  -0.72  -4.3e-01  -2.3e-01   3825    13143      1.0
+#> log_lik[5]      -1.2e+00  4.1e-03    0.28   -1.7  -1.2e+00  -7.6e-01   4632    15916      1.0
+#> log_lik[6]      -5.9e-01  2.8e-03    0.19  -0.93  -5.7e-01  -3.2e-01   4602    15813      1.0
+#> log_lik[7]      -6.4e-01  1.9e-03    0.13  -0.86  -6.3e-01  -4.5e-01   4425    15206      1.0
+#> log_lik[8]      -2.7e-01  2.3e-03    0.14  -0.54  -2.5e-01  -1.0e-01   3551    12204      1.0
+#> log_lik[9]      -6.9e-01  2.6e-03    0.17  -0.99  -6.8e-01  -4.4e-01   4396    15105      1.0
+#> log_lik[10]     -7.5e-01  4.0e-03    0.24   -1.2  -7.2e-01  -4.1e-01   3524    12111      1.0
+#> log_lik[11]     -2.8e-01  1.9e-03    0.12  -0.51  -2.6e-01  -1.2e-01   4067    13978     1.00
+#> log_lik[12]     -5.0e-01  3.5e-03    0.24  -0.94  -4.6e-01  -1.9e-01   4672    16056     1.00
+#> log_lik[13]     -6.6e-01  3.0e-03    0.21   -1.0  -6.4e-01  -3.6e-01   4681    16086      1.0
+#> log_lik[14]     -3.6e-01  2.9e-03    0.17  -0.69  -3.3e-01  -1.4e-01   3667    12602      1.0
+#> log_lik[15]     -2.8e-01  1.6e-03    0.11  -0.47  -2.6e-01  -1.4e-01   4253    14617      1.0
+#> log_lik[16]     -2.7e-01  1.5e-03   0.087  -0.43  -2.6e-01  -1.5e-01   3572    12276      1.0
+#> log_lik[17]     -1.6e+00  4.6e-03    0.29   -2.1  -1.6e+00  -1.2e+00   3940    13539     1.00
+#> log_lik[18]     -4.8e-01  1.7e-03    0.11  -0.68  -4.7e-01  -3.1e-01   4208    14461      1.0
+#> log_lik[19]     -2.3e-01  1.3e-03   0.075  -0.37  -2.2e-01  -1.2e-01   3488    11985     1.00
+#> log_lik[20]     -1.1e-01  1.3e-03   0.078  -0.26  -9.3e-02  -2.8e-02   3400    11685      1.0
+#> log_lik[21]     -2.1e-01  1.4e-03   0.086  -0.37  -2.0e-01  -9.3e-02   3747    12875      1.0
+#> log_lik[22]     -5.7e-01  2.3e-03    0.15  -0.84  -5.5e-01  -3.5e-01   4159    14294      1.0
+#> log_lik[23]     -3.3e-01  2.3e-03    0.14  -0.60  -3.0e-01  -1.4e-01   3876    13319      1.0
+#> log_lik[24]     -1.3e-01  1.1e-03   0.066  -0.26  -1.2e-01  -5.1e-02   3311    11379      1.0
+#> log_lik[25]     -4.5e-01  1.9e-03    0.12  -0.67  -4.4e-01  -2.7e-01   4263    14650      1.0
+#> log_lik[26]     -1.5e+00  4.9e-03    0.34   -2.1  -1.5e+00  -1.0e+00   4863    16712     1.00
+#> log_lik[27]     -3.0e-01  1.9e-03    0.12  -0.53  -2.9e-01  -1.4e-01   3942    13545      1.0
+#> log_lik[28]     -4.4e-01  1.3e-03   0.085  -0.59  -4.4e-01  -3.1e-01   4011    13785      1.0
+#> log_lik[29]     -7.2e-01  3.4e-03    0.23   -1.1  -6.9e-01  -3.9e-01   4401    15125      1.0
+#> log_lik[30]     -6.9e-01  2.9e-03    0.20   -1.0  -6.7e-01  -4.0e-01   4483    15406      1.0
+#> log_lik[31]     -4.9e-01  2.5e-03    0.16  -0.78  -4.7e-01  -2.6e-01   4140    14226      1.0
+#> log_lik[32]     -4.2e-01  1.6e-03    0.11  -0.61  -4.1e-01  -2.7e-01   4397    15110      1.0
+#> log_lik[33]     -4.1e-01  1.9e-03    0.13  -0.64  -4.0e-01  -2.2e-01   4495    15446      1.0
+#> log_lik[34]     -6.2e-02  8.6e-04   0.050  -0.16  -4.8e-02  -1.2e-02   3377    11605      1.0
+#> log_lik[35]     -5.8e-01  2.5e-03    0.18  -0.90  -5.6e-01  -3.2e-01   5084    17471     1.00
+#> log_lik[36]     -3.2e-01  2.0e-03    0.13  -0.56  -3.0e-01  -1.4e-01   4311    14814      1.0
+#> log_lik[37]     -7.1e-01  3.7e-03    0.23   -1.1  -6.8e-01  -3.8e-01   3857    13253      1.0
+#> log_lik[38]     -3.1e-01  2.3e-03    0.15  -0.58  -2.8e-01  -1.2e-01   3910    13435      1.0
+#> log_lik[39]     -1.8e-01  1.8e-03    0.11  -0.39  -1.5e-01  -5.3e-02   3559    12229      1.0
+#> log_lik[40]     -6.8e-01  2.0e-03    0.13  -0.92  -6.7e-01  -4.8e-01   4390    15085      1.0
+#> log_lik[41]     -1.1e+00  4.2e-03    0.26   -1.6  -1.1e+00  -7.5e-01   3790    13024      1.0
+#> log_lik[42]     -9.3e-01  3.1e-03    0.20   -1.3  -9.1e-01  -6.3e-01   4446    15278      1.0
+#> log_lik[43]     -4.0e-01  3.5e-03    0.25  -0.89  -3.5e-01  -1.0e-01   5239    18003     1.00
+#> log_lik[44]     -1.2e+00  3.0e-03    0.19   -1.5  -1.2e+00  -8.9e-01   3904    13415      1.0
+#> log_lik[45]     -3.6e-01  1.8e-03    0.12  -0.57  -3.4e-01  -1.9e-01   4092    14061      1.0
+#> log_lik[46]     -5.8e-01  2.0e-03    0.13  -0.82  -5.7e-01  -3.8e-01   4539    15598      1.0
+#> log_lik[47]     -3.0e-01  2.0e-03    0.13  -0.55  -2.8e-01  -1.3e-01   4158    14288      1.0
+#> log_lik[48]     -3.2e-01  1.3e-03   0.083  -0.47  -3.1e-01  -2.0e-01   4285    14726     1.00
+#> log_lik[49]     -3.2e-01  1.3e-03   0.080  -0.46  -3.1e-01  -2.0e-01   3835    13178      1.0
+#> log_lik[50]     -1.3e+00  4.6e-03    0.33   -1.9  -1.3e+00  -8.0e-01   5110    17559     1.00
+#> log_lik[51]     -2.9e-01  1.4e-03   0.094  -0.45  -2.7e-01  -1.5e-01   4316    14833     1.00
+#> log_lik[52]     -8.3e-01  2.3e-03    0.14   -1.1  -8.3e-01  -6.1e-01   4123    14167      1.0
+#> log_lik[53]     -4.0e-01  2.0e-03    0.13  -0.64  -3.8e-01  -2.2e-01   3978    13669      1.0
+#> log_lik[54]     -3.7e-01  2.2e-03    0.14  -0.63  -3.5e-01  -1.8e-01   4204    14447      1.0
+#> log_lik[55]     -3.9e-01  2.0e-03    0.14  -0.64  -3.7e-01  -1.9e-01   4560    15671      1.0
+#> log_lik[56]     -3.1e-01  2.9e-03    0.19  -0.68  -2.7e-01  -8.9e-02   4172    14335      1.0
+#> log_lik[57]     -6.6e-01  1.9e-03    0.12  -0.87  -6.5e-01  -4.8e-01   4000    13747      1.0
+#> log_lik[58]     -9.6e-01  5.3e-03    0.35   -1.6  -9.1e-01  -4.6e-01   4421    15193      1.0
+#> log_lik[59]     -1.4e+00  5.3e-03    0.35   -2.0  -1.3e+00  -8.4e-01   4356    14970      1.0
+#> log_lik[60]     -9.8e-01  2.5e-03    0.16   -1.3  -9.7e-01  -7.3e-01   4051    13921      1.0
+#> log_lik[61]     -5.4e-01  1.5e-03    0.10  -0.71  -5.3e-01  -3.9e-01   4197    14422      1.0
+#> log_lik[62]     -8.8e-01  4.6e-03    0.31   -1.4  -8.4e-01  -4.4e-01   4561    15674     1.00
+#> log_lik[63]     -1.1e-01  1.3e-03   0.072  -0.26  -9.8e-02  -3.2e-02   3248    11162      1.0
+#> log_lik[64]     -9.1e-01  3.9e-03    0.26   -1.4  -8.9e-01  -5.3e-01   4383    15063      1.0
+#> log_lik[65]     -2.0e+00  9.5e-03    0.59   -3.0  -2.0e+00  -1.1e+00   3828    13154     1.00
+#> log_lik[66]     -5.1e-01  2.0e-03    0.13  -0.75  -5.0e-01  -3.1e-01   4591    15777      1.0
+#> log_lik[67]     -2.7e-01  1.3e-03   0.082  -0.42  -2.7e-01  -1.6e-01   3737    12841     1.00
+#> log_lik[68]     -1.1e+00  3.9e-03    0.24   -1.5  -1.1e+00  -7.0e-01   3952    13582      1.0
+#> log_lik[69]     -4.4e-01  1.3e-03   0.085  -0.59  -4.3e-01  -3.1e-01   3944    13552      1.0
+#> log_lik[70]     -6.3e-01  3.4e-03    0.24   -1.1  -6.0e-01  -3.0e-01   5018    17243      1.0
+#> log_lik[71]     -6.1e-01  2.8e-03    0.21  -0.98  -5.9e-01  -3.2e-01   5227    17963     1.00
+#> log_lik[72]     -4.6e-01  2.6e-03    0.17  -0.78  -4.4e-01  -2.3e-01   4552    15644      1.0
+#> log_lik[73]     -1.5e+00  5.9e-03    0.37   -2.2  -1.5e+00  -9.4e-01   3999    13742      1.0
+#> log_lik[74]     -9.4e-01  3.0e-03    0.19   -1.3  -9.3e-01  -6.5e-01   4092    14063      1.0
+#> log_lik[75]     -1.1e+00  5.6e-03    0.38   -1.8  -1.1e+00  -5.9e-01   4639    15941     1.00
+#> log_lik[76]     -3.7e-01  2.3e-03    0.14  -0.63  -3.5e-01  -1.8e-01   3728    12812      1.0
+#> log_lik[77]     -8.8e-01  2.2e-03    0.14   -1.1  -8.7e-01  -6.6e-01   4154    14274      1.0
+#> log_lik[78]     -4.9e-01  2.4e-03    0.17  -0.80  -4.7e-01  -2.5e-01   4939    16971     1.00
+#> log_lik[79]     -7.7e-01  3.2e-03    0.19   -1.1  -7.5e-01  -4.9e-01   3734    12831      1.0
+#> log_lik[80]     -5.5e-01  2.8e-03    0.20  -0.91  -5.2e-01  -2.7e-01   5052    17361      1.0
+#> log_lik[81]     -1.6e-01  1.6e-03   0.100  -0.35  -1.4e-01  -4.6e-02   4042    13890      1.0
+#> log_lik[82]     -2.2e-01  2.1e-03    0.14  -0.50  -1.9e-01  -6.1e-02   4473    15371      1.0
+#> log_lik[83]     -3.4e-01  1.3e-03   0.081  -0.49  -3.4e-01  -2.2e-01   3946    13559      1.0
+#> log_lik[84]     -2.8e-01  1.4e-03   0.091  -0.44  -2.6e-01  -1.5e-01   4018    13807      1.0
+#> log_lik[85]     -1.3e-01  1.3e-03   0.074  -0.27  -1.1e-01  -4.2e-02   3489    11990      1.0
+#> log_lik[86]     -1.1e+00  4.7e-03    0.32   -1.7  -1.1e+00  -6.4e-01   4742    16295      1.0
+#> log_lik[87]     -8.3e-01  2.0e-03    0.13   -1.0  -8.2e-01  -6.2e-01   4289    14739      1.0
+#> log_lik[88]     -7.8e-01  3.5e-03    0.24   -1.2  -7.5e-01  -4.3e-01   4606    15828     1.00
+#> log_lik[89]     -1.3e+00  4.9e-03    0.32   -1.9  -1.3e+00  -7.9e-01   4346    14936      1.0
+#> log_lik[90]     -2.6e-01  2.1e-03    0.13  -0.51  -2.3e-01  -9.0e-02   4192    14407     1.00
+#> log_lik[91]     -3.9e-01  1.9e-03    0.13  -0.62  -3.7e-01  -2.0e-01   4638    15940      1.0
+#> log_lik[92]     -1.5e+00  5.2e-03    0.34   -2.1  -1.5e+00  -9.9e-01   4259    14636      1.0
+#> log_lik[93]     -7.3e-01  3.6e-03    0.22   -1.1  -7.1e-01  -4.2e-01   3742    12858      1.0
+#> log_lik[94]     -3.2e-01  1.4e-03   0.088  -0.48  -3.1e-01  -1.9e-01   3956    13596      1.0
+#> log_lik[95]     -3.9e-01  1.8e-03    0.11  -0.59  -3.8e-01  -2.3e-01   3846    13217      1.0
+#> log_lik[96]     -1.6e+00  4.5e-03    0.28   -2.1  -1.6e+00  -1.1e+00   3868    13293      1.0
+#> log_lik[97]     -4.3e-01  1.5e-03    0.10  -0.61  -4.2e-01  -2.7e-01   4724    16235      1.0
+#> log_lik[98]     -1.1e+00  5.5e-03    0.37   -1.7  -1.0e+00  -5.2e-01   4560    15668      1.0
+#> log_lik[99]     -7.0e-01  2.2e-03    0.15  -0.95  -6.8e-01  -4.7e-01   4389    15084      1.0
+#> log_lik[100]    -3.9e-01  1.5e-03   0.097  -0.56  -3.8e-01  -2.5e-01   4436    15244     1.00
 #> 
 #> Samples were drawn using hmc with nuts.
 #> For each parameter, N_Eff is a crude measure of effective sample size,
diff --git a/docs/reference/fit-method-code.html b/docs/reference/fit-method-code.html
index 5dc9cdc2..e2080e07 100644
--- a/docs/reference/fit-method-code.html
+++ b/docs/reference/fit-method-code.html
@@ -17,7 +17,7 @@
       
       
         cmdstanr
-        0.6.0
+        0.7.0
    diff --git a/docs/reference/fit-method-constrain_variables.html b/docs/reference/fit-method-constrain_variables.html index 79cdb61a..122d2d4b 100644 --- a/docs/reference/fit-method-constrain_variables.html +++ b/docs/reference/fit-method-constrain_variables.html @@ -18,7 +18,7 @@ cmdstanr - 0.6.0 + 0.7.0 @@ -145,7 +145,6 @@

    See also

    Examples

    # \dontrun{
 fit_mcmc <- cmdstanr_example("logistic", method = "sample", force_recompile = TRUE)
-#> Compiling Stan program...
 fit_mcmc$init_model_methods()
 #> Compiling additional model methods...
 fit_mcmc$constrain_variables(unconstrained_variables = c(0.5, 1.2, 1.1, 2.2))
diff --git a/docs/reference/fit-method-diagnostic_summary.html b/docs/reference/fit-method-diagnostic_summary.html
index c7c0ce32..97dce2c2 100644
--- a/docs/reference/fit-method-diagnostic_summary.html
+++ b/docs/reference/fit-method-diagnostic_summary.html
@@ -24,7 +24,7 @@
       
       
         cmdstanr
-        0.6.0
+        0.7.0
    @@ -161,29 +161,34 @@

    See also

    Examples

    # \dontrun{
 fit <- cmdstanr_example("schools")
-#> Warning: 104 of 4000 (3.0%) transitions ended with a divergence.
+#> Chain 2 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
+#> Chain 2 Exception: normal_lpdf: Scale parameter is 0, but must be positive! (in '/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/model-11d99572dc9ce.stan', line 14, column 2 to column 41)
+#> Chain 2 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
+#> Chain 2 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
+#> Chain 2 
+#> Warning: 103 of 4000 (3.0%) transitions ended with a divergence.
 #> See https://mc-stan.org/misc/warnings for details.
 fit$diagnostic_summary()
-#> Warning: 104 of 4000 (3.0%) transitions ended with a divergence.
+#> Warning: 103 of 4000 (3.0%) transitions ended with a divergence.
 #> See https://mc-stan.org/misc/warnings for details.
 #> $num_divergent
-#> [1] 47 52  3  2
+#> [1]  2 40 38 23
 #> 
 #> $num_max_treedepth
 #> [1] 0 0 0 0
 #> 
 #> $ebfmi
-#> [1] 0.3009803 0.3412086 0.3406291 0.2824946
+#> [1] 0.2865692 0.3867145 0.2763028 0.3720129
 #> 
 fit$diagnostic_summary(quiet = TRUE)
 #> $num_divergent
-#> [1] 47 52  3  2
+#> [1]  2 40 38 23
 #> 
 #> $num_max_treedepth
 #> [1] 0 0 0 0
 #> 
 #> $ebfmi
-#> [1] 0.3009803 0.3412086 0.3406291 0.2824946
+#> [1] 0.2865692 0.3867145 0.2763028 0.3720129
 #> 
 # }
 
diff --git a/docs/reference/fit-method-draws-1.png b/docs/reference/fit-method-draws-1.png
index 74c1b4fb58c273eee1af12c160a2dced550386f5..27dbcc215edcba84e9ddad214c0c299681bbcec6 100644
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diff --git a/docs/reference/fit-method-draws.html b/docs/reference/fit-method-draws.html
index 0fd6cc76..1a2ac8f7 100644
--- a/docs/reference/fit-method-draws.html
+++ b/docs/reference/fit-method-draws.html
@@ -22,7 +22,7 @@
       
       
         cmdstanr
-        0.6.0
+        0.7.0
    @@ -203,7 +203,7 @@

    Examples

    dim(draws) #> [1] 1000 4 105 str(draws) -#> 'draws_array' num [1:1000, 1:4, 1:105] -66.2 -64.2 -65.5 -66.1 -64.4 ... +#> 'draws_array' num [1:1000, 1:4, 1:105] -65.8 -67 -66.3 -66.2 -66.8 ... #> - attr(*, "dimnames")=List of 3 #> ..$ iteration: chr [1:1000] "1" "2" "3" "4" ... #> ..$ chain : chr [1:4] "1" "2" "3" "4" @@ -215,12 +215,12 @@

    Examples

    #> # A draws_matrix: 6 iterations, 1 chains, and 105 variables #> variable #> draw lp__ alpha beta[1] beta[2] beta[3] log_lik[1] log_lik[2] log_lik[3] -#> 1 -66 0.098 -0.85 -0.59 0.74 -0.65 -0.30 -0.84 -#> 2 -64 0.291 -0.57 -0.31 0.52 -0.55 -0.45 -0.58 -#> 3 -65 0.185 -0.32 -0.28 0.82 -0.65 -0.32 -0.62 -#> 4 -66 0.223 -0.38 -0.14 0.97 -0.62 -0.25 -0.44 -#> 5 -64 0.313 -0.65 -0.14 0.83 -0.53 -0.28 -0.37 -#> 6 -64 0.338 -0.74 -0.20 0.80 -0.52 -0.30 -0.39 +#> 1 -66 0.24 -0.55 0.09 0.39 -0.52 -0.42 -0.32 +#> 2 -67 0.52 -0.85 -0.20 1.28 -0.46 -0.18 -0.26 +#> 3 -66 0.19 -0.80 -0.31 0.20 -0.54 -0.53 -0.62 +#> 4 -66 0.39 -0.57 -0.68 1.03 -0.59 -0.33 -0.81 +#> 5 -67 0.46 -0.49 -0.63 1.16 -0.57 -0.30 -0.72 +#> 6 -65 0.30 -0.44 -0.21 0.28 -0.54 -0.59 -0.56 #> # ... with 97 more variables # or can specify 'format' argument to avoid manual conversion @@ -230,12 +230,12 @@

    Examples

    #> # A draws_matrix: 6 iterations, 1 chains, and 105 variables #> variable #> draw lp__ alpha beta[1] beta[2] beta[3] log_lik[1] log_lik[2] log_lik[3] -#> 1 -66 0.098 -0.85 -0.59 0.74 -0.65 -0.30 -0.84 -#> 2 -64 0.291 -0.57 -0.31 0.52 -0.55 -0.45 -0.58 -#> 3 -65 0.185 -0.32 -0.28 0.82 -0.65 -0.32 -0.62 -#> 4 -66 0.223 -0.38 -0.14 0.97 -0.62 -0.25 -0.44 -#> 5 -64 0.313 -0.65 -0.14 0.83 -0.53 -0.28 -0.37 -#> 6 -64 0.338 -0.74 -0.20 0.80 -0.52 -0.30 -0.39 +#> 1 -66 0.24 -0.55 0.09 0.39 -0.52 -0.42 -0.32 +#> 2 -67 0.52 -0.85 -0.20 1.28 -0.46 -0.18 -0.26 +#> 3 -66 0.19 -0.80 -0.31 0.20 -0.54 -0.53 -0.62 +#> 4 -66 0.39 -0.57 -0.68 1.03 -0.59 -0.33 -0.81 +#> 5 -67 0.46 -0.49 -0.63 1.16 -0.57 -0.30 -0.72 +#> 6 -65 0.30 -0.44 -0.21 0.28 -0.54 -0.59 -0.56 #> # ... with 97 more variables # can select specific parameters @@ -244,12 +244,12 @@

    Examples

    #> , , variable = alpha #> #> chain -#> iteration 1 2 3 4 -#> 1 0.098 0.043 0.30 0.119 -#> 2 0.291 0.133 0.26 0.609 -#> 3 0.185 0.612 0.11 0.483 -#> 4 0.223 0.561 0.43 0.842 -#> 5 0.313 0.463 0.21 0.044 +#> iteration 1 2 3 4 +#> 1 0.24 0.310 0.180 0.67 +#> 2 0.52 -0.014 0.351 0.23 +#> 3 0.19 0.366 0.682 0.48 +#> 4 0.39 0.645 0.098 0.49 +#> 5 0.46 0.177 0.677 0.65 #> #> # ... with 995 more iterations fit$draws("beta") # selects entire vector beta @@ -258,31 +258,31 @@

    Examples

    #> #> chain #> iteration 1 2 3 4 -#> 1 -0.85 -1.03 -1.02 -0.77 -#> 2 -0.57 -0.76 -0.92 -0.44 -#> 3 -0.32 -0.65 -0.94 -0.31 -#> 4 -0.38 -0.95 -0.54 -0.49 -#> 5 -0.65 -0.91 -0.28 -0.75 +#> 1 -0.55 -1.00 -0.41 -0.55 +#> 2 -0.85 -0.45 -0.62 -0.91 +#> 3 -0.80 -0.52 -0.86 -0.50 +#> 4 -0.57 -0.46 -0.50 -0.57 +#> 5 -0.49 -0.97 -1.00 -0.76 #> #> , , variable = beta[2] #> #> chain -#> iteration 1 2 3 4 -#> 1 -0.59 -0.120 -0.206 0.053 -#> 2 -0.31 -0.066 -0.353 -0.424 -#> 3 -0.28 -0.552 0.039 -0.239 -#> 4 -0.14 -0.550 -0.715 -0.513 -#> 5 -0.14 -0.153 0.147 -0.444 +#> iteration 1 2 3 4 +#> 1 0.09 -0.32 -0.132 -0.091 +#> 2 -0.20 -0.45 -0.283 -0.479 +#> 3 -0.31 -0.25 -0.335 -0.681 +#> 4 -0.68 -0.25 0.093 -0.717 +#> 5 -0.63 -0.23 -0.227 -0.328 #> #> , , variable = beta[3] #> #> chain #> iteration 1 2 3 4 -#> 1 0.74 0.61 0.61 0.58 -#> 2 0.52 0.57 0.52 0.83 -#> 3 0.82 0.72 0.68 1.21 -#> 4 0.97 0.59 0.74 1.03 -#> 5 0.83 0.57 0.46 0.52 +#> 1 0.39 0.47 0.54 0.70 +#> 2 1.28 0.68 0.76 0.83 +#> 3 0.20 0.45 0.66 0.30 +#> 4 1.03 0.30 0.53 0.54 +#> 5 1.16 1.17 0.46 1.29 #> #> # ... with 995 more iterations fit$draws(c("alpha", "beta[2]")) @@ -290,22 +290,22 @@

    Examples

    #> , , variable = alpha #> #> chain -#> iteration 1 2 3 4 -#> 1 0.098 0.043 0.30 0.119 -#> 2 0.291 0.133 0.26 0.609 -#> 3 0.185 0.612 0.11 0.483 -#> 4 0.223 0.561 0.43 0.842 -#> 5 0.313 0.463 0.21 0.044 +#> iteration 1 2 3 4 +#> 1 0.24 0.310 0.180 0.67 +#> 2 0.52 -0.014 0.351 0.23 +#> 3 0.19 0.366 0.682 0.48 +#> 4 0.39 0.645 0.098 0.49 +#> 5 0.46 0.177 0.677 0.65 #> #> , , variable = beta[2] #> #> chain -#> iteration 1 2 3 4 -#> 1 -0.59 -0.120 -0.206 0.053 -#> 2 -0.31 -0.066 -0.353 -0.424 -#> 3 -0.28 -0.552 0.039 -0.239 -#> 4 -0.14 -0.550 -0.715 -0.513 -#> 5 -0.14 -0.153 0.147 -0.444 +#> iteration 1 2 3 4 +#> 1 0.09 -0.32 -0.132 -0.091 +#> 2 -0.20 -0.45 -0.283 -0.479 +#> 3 -0.31 -0.25 -0.335 -0.681 +#> 4 -0.68 -0.25 0.093 -0.717 +#> 5 -0.63 -0.23 -0.227 -0.328 #> #> # ... with 995 more iterations @@ -323,21 +323,21 @@

    Examples

    #> # A draws_matrix: 6 iterations, 1 chains, and 3 variables #> variable #> draw beta[1] beta[2] beta[3] -#> 1 -0.66 0.3552 1.03 -#> 2 -0.39 -0.0109 0.32 -#> 3 -0.65 -0.8123 0.59 -#> 4 -0.48 0.0005 0.86 -#> 5 -0.60 -0.4373 0.48 -#> 6 -1.04 -0.0197 1.13 +#> 1 -0.74 0.09 0.55 +#> 2 -0.76 0.39 0.68 +#> 3 -0.43 -0.15 1.06 +#> 4 -1.15 -0.35 0.54 +#> 5 -0.60 -0.26 0.34 +#> 6 -0.30 -0.24 1.03 head(fit$draws("beta", format = "df")) #> # A draws_df: 6 iterations, 1 chains, and 3 variables #> beta[1] beta[2] beta[3] -#> 1 -0.66 0.3552 1.03 -#> 2 -0.39 -0.0109 0.32 -#> 3 -0.65 -0.8123 0.59 -#> 4 -0.48 0.0005 0.86 -#> 5 -0.60 -0.4373 0.48 -#> 6 -1.04 -0.0197 1.13 +#> 1 -0.74 0.09 0.55 +#> 2 -0.76 0.39 0.68 +#> 3 -0.43 -0.15 1.06 +#> 4 -1.15 -0.35 0.54 +#> 5 -0.60 -0.26 0.34 +#> 6 -0.30 -0.24 1.03 #> # ... hidden reserved variables {'.chain', '.iteration', '.draw'} # } diff --git a/docs/reference/fit-method-grad_log_prob.html b/docs/reference/fit-method-grad_log_prob.html index 41213cd7..79de7ede 100644 --- a/docs/reference/fit-method-grad_log_prob.html +++ b/docs/reference/fit-method-grad_log_prob.html @@ -20,7 +20,7 @@ cmdstanr - 0.6.0 + 0.7.0 @@ -139,7 +139,6 @@

    See also

    Examples

    # \dontrun{
 fit_mcmc <- cmdstanr_example("logistic", method = "sample", force_recompile = TRUE)
-#> Compiling Stan program...
 fit_mcmc$init_model_methods()
 #> Compiling additional model methods...
 fit_mcmc$grad_log_prob(unconstrained_variables = c(0.5, 1.2, 1.1, 2.2))
diff --git a/docs/reference/fit-method-gradients.html b/docs/reference/fit-method-gradients.html
index e022df32..828d0688 100644
--- a/docs/reference/fit-method-gradients.html
+++ b/docs/reference/fit-method-gradients.html
@@ -18,7 +18,7 @@
       
       
         cmdstanr
-        0.6.0
+        0.7.0
    @@ -132,10 +132,10 @@

    Examples

    # retrieve the gradients test$gradients() #> param_idx value model finite_diff error -#> 1 0 0.788454 -2.90866 -2.90866 4.89447e-09 -#> 2 1 0.538042 -17.17950 -17.17950 2.76466e-08 -#> 3 2 -1.725840 16.99630 16.99630 8.87535e-10 -#> 4 3 1.854370 -7.63726 -7.63726 -8.95156e-09 +#> 1 0 1.376210 -19.10810 -19.10810 5.51201e-09 +#> 2 1 -0.796954 -2.04336 -2.04336 5.51900e-09 +#> 3 2 0.520069 -11.77830 -11.77830 -6.60224e-10 +#> 4 3 0.122833 7.41794 7.41794 4.62926e-08 # }     diff --git a/docs/reference/fit-method-hessian.html b/docs/reference/fit-method-hessian.html index eaecb8c8..b08bb4ea 100644 --- a/docs/reference/fit-method-hessian.html +++ b/docs/reference/fit-method-hessian.html @@ -20,7 +20,7 @@ cmdstanr - 0.6.0 + 0.7.0 @@ -139,7 +139,6 @@

    See also

    Examples

    # \dontrun{
 fit_mcmc <- cmdstanr_example("logistic", method = "sample", force_recompile = TRUE)
-#> Compiling Stan program...
 # fit_mcmc$init_model_methods(hessian = TRUE)
 # fit_mcmc$hessian(unconstrained_variables = c(0.5, 1.2, 1.1, 2.2))
 # }
diff --git a/docs/reference/fit-method-init.html b/docs/reference/fit-method-init.html
index e66e4808..e6001ccc 100644
--- a/docs/reference/fit-method-init.html
+++ b/docs/reference/fit-method-init.html
@@ -22,7 +22,7 @@
       
       
         cmdstanr
-        0.6.0
+        0.7.0
    @@ -140,11 +140,11 @@

    Examples

    str(fit$init()) #> List of 2 #> $ :List of 2 -#> ..$ alpha: num -1.25 -#> ..$ beta : num [1:3] -2.2653 0.0495 0.8638 +#> ..$ alpha: num 0.865 +#> ..$ beta : num [1:3] -0.0306 -0.1349 1.1272 #> $ :List of 2 -#> ..$ alpha: num -0.172 -#> ..$ beta : num [1:3] 0.437 0.285 1.047 +#> ..$ alpha: num -1.41 +#> ..$ beta : num [1:3] 0.143 -1.043 0.9 # partial inits (only specifying for a subset of parameters) init_list <- list( diff --git a/docs/reference/fit-method-init_model_methods.html b/docs/reference/fit-method-init_model_methods.html index 0b0aa7df..d27c2170 100644 --- a/docs/reference/fit-method-init_model_methods.html +++ b/docs/reference/fit-method-init_model_methods.html @@ -26,7 +26,7 @@ cmdstanr - 0.6.0 + 0.7.0 @@ -153,7 +153,6 @@

    See also

    Examples

    # \dontrun{
 fit_mcmc <- cmdstanr_example("logistic", method = "sample", force_recompile = TRUE)
-#> Compiling Stan program...
 fit_mcmc$init_model_methods()
 #> Compiling additional model methods...
 # }
diff --git a/docs/reference/fit-method-inv_metric.html b/docs/reference/fit-method-inv_metric.html
index cca5c0ce..feac9970 100644
--- a/docs/reference/fit-method-inv_metric.html
+++ b/docs/reference/fit-method-inv_metric.html
@@ -17,7 +17,7 @@
       
       
         cmdstanr
-        0.6.0
+        0.7.0
    @@ -140,75 +140,75 @@

    Examples

    fit$inv_metric() #> $`1` #> [,1] [,2] [,3] [,4] -#> [1,] 0.0431002 0.0000000 0.0000000 0.0000000 -#> [2,] 0.0000000 0.0583438 0.0000000 0.0000000 -#> [3,] 0.0000000 0.0000000 0.0531465 0.0000000 -#> [4,] 0.0000000 0.0000000 0.0000000 0.0786797 +#> [1,] 0.0461164 0.0000000 0.0000000 0.0000000 +#> [2,] 0.0000000 0.0586249 0.0000000 0.0000000 +#> [3,] 0.0000000 0.0000000 0.0569083 0.0000000 +#> [4,] 0.0000000 0.0000000 0.0000000 0.0695349 #> #> $`2` #> [,1] [,2] [,3] [,4] -#> [1,] 0.0454991 0.0000000 0.0000000 0.0000000 -#> [2,] 0.0000000 0.0809962 0.0000000 0.0000000 -#> [3,] 0.0000000 0.0000000 0.0496686 0.0000000 -#> [4,] 0.0000000 0.0000000 0.0000000 0.0688616 +#> [1,] 0.0423827 0.0000000 0.0000000 0.0000000 +#> [2,] 0.0000000 0.0638639 0.0000000 0.0000000 +#> [3,] 0.0000000 0.0000000 0.0463094 0.0000000 +#> [4,] 0.0000000 0.0000000 0.0000000 0.0646605 #> #> $`3` -#> [,1] [,2] [,3] [,4] -#> [1,] 0.0406769 0.000000 0.0000000 0.0000000 -#> [2,] 0.0000000 0.054686 0.0000000 0.0000000 -#> [3,] 0.0000000 0.000000 0.0544764 0.0000000 -#> [4,] 0.0000000 0.000000 0.0000000 0.0675343 +#> [,1] [,2] [,3] [,4] +#> [1,] 0.0555959 0.0000000 0.0000000 0.0000000 +#> [2,] 0.0000000 0.0595803 0.0000000 0.0000000 +#> [3,] 0.0000000 0.0000000 0.0515264 0.0000000 +#> [4,] 0.0000000 0.0000000 0.0000000 0.0759908 #> #> $`4` -#> [,1] [,2] [,3] [,4] -#> [1,] 0.0434081 0.0000000 0.0000000 0.0000000 -#> [2,] 0.0000000 0.0669723 0.0000000 0.0000000 -#> [3,] 0.0000000 0.0000000 0.0466104 0.0000000 -#> [4,] 0.0000000 0.0000000 0.0000000 0.0654703 +#> [,1] [,2] [,3] [,4] +#> [1,] 0.05104 0.000000 0.0000000 0.00000 +#> [2,] 0.00000 0.051252 0.0000000 0.00000 +#> [3,] 0.00000 0.000000 0.0567318 0.00000 +#> [4,] 0.00000 0.000000 0.0000000 0.06699 #> fit$inv_metric(matrix=FALSE) #> $`1` -#> [1] 0.0431002 0.0583438 0.0531465 0.0786797 +#> [1] 0.0461164 0.0586249 0.0569083 0.0695349 #> #> $`2` -#> [1] 0.0454991 0.0809962 0.0496686 0.0688616 +#> [1] 0.0423827 0.0638639 0.0463094 0.0646605 #> #> $`3` -#> [1] 0.0406769 0.0546860 0.0544764 0.0675343 +#> [1] 0.0555959 0.0595803 0.0515264 0.0759908 #> #> $`4` -#> [1] 0.0434081 0.0669723 0.0466104 0.0654703 +#> [1] 0.0510400 0.0512520 0.0567318 0.0669900 #> fit <- cmdstanr_example("logistic", metric = "dense_e") fit$inv_metric() #> $`1` -#> [,1] [,2] [,3] [,4] -#> [1,] 4.31917e-02 0.00404353 0.00504066 9.28594e-06 -#> [2,] 4.04353e-03 0.05741780 -0.00262368 -9.31332e-03 -#> [3,] 5.04066e-03 -0.00262368 0.04816930 -1.40772e-02 -#> [4,] 9.28594e-06 -0.00931332 -0.01407720 7.29028e-02 +#> [,1] [,2] [,3] [,4] +#> [1,] 0.048223400 -0.00737038 0.000297676 0.00680596 +#> [2,] -0.007370380 0.05440320 -0.006681250 -0.00162053 +#> [3,] 0.000297676 -0.00668125 0.043576600 -0.00859282 +#> [4,] 0.006805960 -0.00162053 -0.008592820 0.06856220 #> #> $`2` #> [,1] [,2] [,3] [,4] -#> [1,] 0.05586860 -0.00758105 0.00327655 0.00186063 -#> [2,] -0.00758105 0.06515060 -0.00821290 -0.00560473 -#> [3,] 0.00327655 -0.00821290 0.05351870 -0.01152510 -#> [4,] 0.00186063 -0.00560473 -0.01152510 0.06850340 +#> [1,] 0.04746980 -0.00175084 -0.00118626 0.00664285 +#> [2,] -0.00175084 0.06320920 -0.00172180 -0.01259270 +#> [3,] -0.00118626 -0.00172180 0.04810590 -0.00872089 +#> [4,] 0.00664285 -0.01259270 -0.00872089 0.06689500 #> #> $`3` #> [,1] [,2] [,3] [,4] -#> [1,] 0.05023130 0.00119935 0.00439778 -0.00486186 -#> [2,] 0.00119935 0.06410140 -0.00267351 -0.01118110 -#> [3,] 0.00439778 -0.00267351 0.04945990 -0.01316990 -#> [4,] -0.00486186 -0.01118110 -0.01316990 0.07295030 +#> [1,] 0.04482880 -0.00377658 -0.00343049 0.00304829 +#> [2,] -0.00377658 0.04803130 -0.00957526 -0.00566723 +#> [3,] -0.00343049 -0.00957526 0.05099840 -0.00574551 +#> [4,] 0.00304829 -0.00566723 -0.00574551 0.07092750 #> #> $`4` -#> [,1] [,2] [,3] [,4] -#> [1,] 0.05218850 -0.00108995 0.00814572 0.00609612 -#> [2,] -0.00108995 0.06646310 -0.00283644 -0.01047740 -#> [3,] 0.00814572 -0.00283644 0.05347060 -0.01085170 -#> [4,] 0.00609612 -0.01047740 -0.01085170 0.07278000 +#> [,1] [,2] [,3] [,4] +#> [1,] 0.04787540 -0.003918010 -0.004273680 0.00253868 +#> [2,] -0.00391801 0.049792000 0.000712128 -0.00453177 +#> [3,] -0.00427368 0.000712128 0.045850800 -0.00639672 +#> [4,] 0.00253868 -0.004531770 -0.006396720 0.07511110 #> # } diff --git a/docs/reference/fit-method-log_prob.html b/docs/reference/fit-method-log_prob.html index cac3b586..a0a9f638 100644 --- a/docs/reference/fit-method-log_prob.html +++ b/docs/reference/fit-method-log_prob.html @@ -17,7 +17,7 @@ cmdstanr - 0.6.0 + 0.7.0 @@ -134,7 +134,6 @@

    See also

    Examples

    # \dontrun{
 fit_mcmc <- cmdstanr_example("logistic", method = "sample", force_recompile = TRUE)
-#> Compiling Stan program...
 fit_mcmc$init_model_methods()
 #> Compiling additional model methods...
 fit_mcmc$log_prob(unconstrained_variables = c(0.5, 1.2, 1.1, 2.2))
diff --git a/docs/reference/fit-method-loo.html b/docs/reference/fit-method-loo.html
index 95111bd3..5a1a1743 100644
--- a/docs/reference/fit-method-loo.html
+++ b/docs/reference/fit-method-loo.html
@@ -21,7 +21,7 @@
       
       
         cmdstanr
-        0.6.0
+        0.7.0
    @@ -175,16 +175,15 @@

    Examples

    # \dontrun{ # the "logistic" example model has "log_lik" in generated quantities fit <- cmdstanr_example("logistic") -#> Model executable is up to date! loo_result <- fit$loo(cores = 2) print(loo_result) #> #> Computed from 4000 by 100 log-likelihood matrix #> #> Estimate SE -#> elpd_loo -63.7 4.1 +#> elpd_loo -63.7 4.2 #> p_loo 3.9 0.5 -#> looic 127.4 8.3 +#> looic 127.3 8.3 #> ------ #> Monte Carlo SE of elpd_loo is 0.0. #> diff --git a/docs/reference/fit-method-lp-1.png b/docs/reference/fit-method-lp-1.png index ca30aa56176b9b99fc0993043dce36dfb774ee2a..508c1b8434ca797d52a8a0252b4162895281a24a 100644 GIT binary patch literal 104052 zcmeGDhd-P9|38k4t;0O6)+{ZGqN?^fDlIijjn-CGGd3ZKgf6s3TYHxpt=%Fv={U4i zQk)2hkWyO)K@gFz*Xx|ur~Um6zjtm)p1EDO>$-A#KF0lVe~c^ThKo5NA{eKepKMDMw1pfa=0++@5owm4^r2H(s1iYAz@iPBlpv!y+S1;cMH;m|| znsFD5=u2pkVS;VL%F}2670qZ}xMJ9+n%-nPUsP?jiZfY?uc+lnqv<~v*N!6qwy02u znka`yy^?~^JpG^JoqiBmA!?v*-P;*YvWz1%x4py{K!zrC#24jy#Sjeyh57wVD5%b2;cVwLuKe9?CY&Yv6@K 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zED`P`CRfE^lF$P~c(Z=FOe;?}$)KAj8fu#EqJJ#r$B;YHCI!7I(4BH!Zc|3rB@C>k{LwBp(=hlvb@{%^;_KDX#^$C8YOb?sde)V8#>9z(u>Wf|h%i z(~rtzetrNzk%g}g&mEjE z8s|7`Z*X=u`ZXu>&dAn=W3LP31Jwtz@+tEtPG$c;^Yf1IjKl}R!}{G}9~e{!1g4^+ KTyfjx;r|8d1Hc3T diff --git a/docs/reference/fit-method-lp.html b/docs/reference/fit-method-lp.html index 8aaf6c78..1dd19bc7 100644 --- a/docs/reference/fit-method-lp.html +++ b/docs/reference/fit-method-lp.html @@ -2,7 +2,9 @@ Extract log probability (target) — fit-method-lp • cmdstanrReport timing of CmdStan runs — fit-method-time • cmdstanr @@ -19,7 +21,7 @@ cmdstanr - 0.6.0 + 0.7.0 @@ -108,7 +110,9 @@

    Report timing of CmdStan runs

    Report the run time in seconds. For MCMC additional information is provided about the run times of individual chains and the warmup and -sampling phases.

    +sampling phases. For Laplace approximation the time only include the time +for drawing the approximate sample and does not include the time +taken to run the $optimize() method.

    @@ -136,27 +140,36 @@

    Examples

    fit_mcmc <- cmdstanr_example("logistic", method = "sample") fit_mcmc$time() #> $total -#> [1] 0.553201 +#> [1] 0.5498919 #> #> $chains #> chain_id warmup sampling total -#> 1 1 0.021 0.068 0.089 -#> 2 2 0.022 0.070 0.092 -#> 3 3 0.022 0.076 0.098 -#> 4 4 0.023 0.071 0.094 +#> 1 1 0.023 0.071 0.094 +#> 2 2 0.023 0.070 0.093 +#> 3 3 0.023 0.074 0.097 +#> 4 4 0.023 0.074 0.097 #> -fit_mle <- cmdstanr_example("logistic", method = "optimize") +fit_vb <- cmdstanr_example("logistic", method = "variational") +fit_vb$time() +#> $total +#> [1] 0.1315289 +#> + +fit_mle <- cmdstanr_example("logistic", method = "optimize", jacobian = TRUE) fit_mle$time() #> $total -#> [1] 0.1247039 +#> [1] 0.1302979 #> -fit_vb <- cmdstanr_example("logistic", method = "variational") -fit_vb$time() +# use fit_mle to draw samples from laplace approximation +fit_laplace <- cmdstanr_example("logistic", method = "laplace", mode = fit_mle) +fit_laplace$time() # just time for drawing sample not for running optimize #> $total -#> [1] 0.1268399 +#> [1] 0.136251 #> +fit_laplace$time()$total + fit_mle$time()$total # total time +#> [1] 0.2665489 # }
    diff --git a/docs/reference/fit-method-unconstrain_draws.html b/docs/reference/fit-method-unconstrain_draws.html index 3efbaca4..b849e0ba 100644 --- a/docs/reference/fit-method-unconstrain_draws.html +++ b/docs/reference/fit-method-unconstrain_draws.html @@ -22,7 +22,7 @@ cmdstanr - 0.6.0 + 0.7.0 @@ -143,7 +143,6 @@

    See also

    Examples

    # \dontrun{
 fit_mcmc <- cmdstanr_example("logistic", method = "sample", force_recompile = TRUE)
-#> Compiling Stan program...
 fit_mcmc$init_model_methods()
 #> Compiling additional model methods...
 
diff --git a/docs/reference/fit-method-unconstrain_variables.html b/docs/reference/fit-method-unconstrain_variables.html
index ba0c10ce..f2b96cab 100644
--- a/docs/reference/fit-method-unconstrain_variables.html
+++ b/docs/reference/fit-method-unconstrain_variables.html
@@ -18,7 +18,7 @@
       
       
         cmdstanr
-        0.6.0
+        0.7.0
    @@ -131,7 +131,6 @@

    See also

    Examples

    # \dontrun{
 fit_mcmc <- cmdstanr_example("logistic", method = "sample", force_recompile = TRUE)
-#> Compiling Stan program...
 fit_mcmc$init_model_methods()
 #> Compiling additional model methods...
 fit_mcmc$unconstrain_variables(list(alpha = 0.5, beta = c(0.7, 1.1, 0.2)))
diff --git a/docs/reference/fit-method-variable_skeleton.html b/docs/reference/fit-method-variable_skeleton.html
index 0bc3100e..1fb28b4c 100644
--- a/docs/reference/fit-method-variable_skeleton.html
+++ b/docs/reference/fit-method-variable_skeleton.html
@@ -1,7 +1,7 @@
 
-Return the variable skeleton needed by the utils::relist function to re-structure a
-vector of constrained parameter values to a named list — fit-method-variable_skeleton • cmdstanrReturn the variable skeleton for relist — fit-method-variable_skeleton • cmdstanr
@@ -19,7 +19,7 @@
       
       
         cmdstanr
-        0.6.0
+        0.7.0
    @@ -100,14 +100,15 @@
    -

    Return the variable skeleton needed by the utils::relist function to re-structure a -vector of constrained parameter values to a named list

    +

    Return the variable skeleton for relist

    Source: R/fit.R
    -

    The $variable_skeleton() method returns the variable skeleton

    +

    The $variable_skeleton() method returns the variable skeleton +needed by utils::relist() to re-structure a vector of constrained +parameter values to a named list.

    @@ -117,13 +118,13 @@

    Return the variable skeleton needed by the utils::relist function to re-stru

    Arguments

    transformed_parameters
    -

    (boolean) Whether to include transformed parameters -in the skeleton (defaults to TRUE)

    +

    (logical) Whether to include transformed +parameters in the skeleton (defaults to TRUE).

    generated_quantities
    -

    (boolean) Whether to include generated quantities -in the skeleton (defaults to TRUE)

    +

    (logical) Whether to include generated quantities +in the skeleton (defaults to TRUE).

    @@ -136,11 +137,21 @@

    See also

    Examples

    # \dontrun{
-fit_mcmc <- cmdstanr_example("logistic", method = "sample")
+fit_mcmc <- cmdstanr_example("logistic", method = "sample", force_recompile = TRUE)
 fit_mcmc$init_model_methods()
-#> Error: Model methods cannot be used with a pre-compiled Stan executable, the model must be compiled again
+#> Compiling additional model methods...
 fit_mcmc$variable_skeleton()
-#> Error: The method has not been compiled, please call `init_model_methods()` first
+#> $alpha
+#> [1] 0
+#> 
+#> $beta
+#> [1] 0 0 0
+#> 
+#> $log_lik
+#>   [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+#>  [38] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+#>  [75] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+#> 
 # }
    diff --git a/docs/reference/index.html b/docs/reference/index.html index ad6c2b70..1036551c 100644 --- a/docs/reference/index.html +++ b/docs/reference/index.html @@ -17,7 +17,7 @@ cmdstanr - 0.6.0 + 0.7.0
    @@ -157,10 +157,18 @@

    Running CmdStan from R generate_quantities()

    Run Stan's standalone generated quantities method

    + +

    laplace()

    + +

    Run Stan's laplace algorithm

    optimize()

    Run Stan's optimization algorithms

    + +

    pathfinder()

    + +

    Run Stan's Pathfinder Variational Inference Algorithm

    sample()

    @@ -193,10 +201,18 @@

    Fitted model objects and methods CmdStanMLE

    CmdStanMLE objects

    + +

    CmdStanLaplace

    + +

    CmdStanLaplace objects

    CmdStanVB

    CmdStanVB objects

    + +

    CmdStanPathfinder

    + +

    CmdStanPathfinder objects

    CmdStanGQ

    @@ -261,7 +277,7 @@

    Fitted model objects and methods

    Leave-one-out cross-validation (LOO-CV)

    -

    lp() lp_approx()

    +

    lp() lp_approx() lp_approx()

    Extract log probability (target)

    @@ -350,7 +366,7 @@

    Other tools for working with CmdSt

    Convert CmdStanMCMC to mcmc.list

    -

    as_draws(<CmdStanMCMC>) as_draws(<CmdStanMLE>) as_draws(<CmdStanVB>) as_draws(<CmdStanGQ>)

    +

    as_draws(<CmdStanMCMC>) as_draws(<CmdStanMLE>) as_draws(<CmdStanLaplace>) as_draws(<CmdStanVB>) as_draws(<CmdStanGQ>) as_draws(<CmdStanPathfinder>)

    Create a draws object from a CmdStanR fitted model object

    diff --git a/docs/reference/install_cmdstan.html b/docs/reference/install_cmdstan.html index 5995ec07..f63b9b78 100644 --- a/docs/reference/install_cmdstan.html +++ b/docs/reference/install_cmdstan.html @@ -34,7 +34,7 @@ cmdstanr - 0.6.0 + 0.7.0

    @@ -150,6 +150,7 @@

    Install CmdStan or clean and rebuild an existing installation

    timeout = 1200, version = NULL, release_url = NULL, + release_file = NULL, cpp_options = list(), check_toolchain = TRUE, wsl = FALSE @@ -217,6 +218,13 @@

    Arguments

    If both version and release_url are specified then version will be used.

    +
    release_file
    +

    (string) A file path to a CmdStan release tar.gz file +downloaded from the releases page: https://github.com/stan-dev/cmdstan/releases. +For example: release_file=""./cmdstan-2.33.1.tar.gz". If release_file is +specified then both release_url and version will be ignored.

    + +
    cpp_options

    (list) Any makefile flags/variables to be written to the make/local file. For example, list("CXX" = "clang++") will force diff --git a/docs/reference/model-method-check_syntax.html b/docs/reference/model-method-check_syntax.html index 1d321677..14ad8c59 100644 --- a/docs/reference/model-method-check_syntax.html +++ b/docs/reference/model-method-check_syntax.html @@ -19,7 +19,7 @@ cmdstanr - 0.6.0 + 0.7.0

    @@ -168,7 +168,9 @@

    See also

    model-method-expose_functions, model-method-format, model-method-generate-quantities, +model-method-laplace, model-method-optimize, +model-method-pathfinder, model-method-sample_mpi, model-method-sample, model-method-variables, @@ -181,7 +183,7 @@

    Examples

    file <- write_stan_file(" data { int N; - int y[N]; + array[N] int y; } parameters { // should have <lower=0> but omitting to demonstrate pedantic mode @@ -195,22 +197,12 @@

    Examples

    # the program is syntactically correct, however... mod$check_syntax() -#> Warning in '/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpFBtN6X/model_74b584fd23ea8c78eceda86d1d425fe8.stan', line 4, column 2: Declaration -#> of arrays by placing brackets after a variable name is deprecated and -#> will be removed in Stan 2.33.0. Instead use the array keyword before the -#> type. This can be changed automatically using the auto-format flag to -#> stanc #> Stan program is syntactically correct # pedantic mode will warn that lambda should be constrained to be positive # and that lambda has no prior distribution mod$check_syntax(pedantic = TRUE) -#> Warning in '/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpFBtN6X/model_74b584fd23ea8c78eceda86d1d425fe8.stan', line 4, column 2: Declaration -#> of arrays by placing brackets after a variable name is deprecated and -#> will be removed in Stan 2.33.0. Instead use the array keyword before the -#> type. This can be changed automatically using the auto-format flag to -#> stanc -#> Warning in '/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpFBtN6X/model_74b584fd23ea8c78eceda86d1d425fe8.stan', line 11, column 14: A +#> Warning in '/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/model_febb1e69c7387a0e64cf13583e078104.stan', line 11, column 14: A #> poisson distribution is given parameter lambda as a rate parameter #> (argument 1), but lambda was not constrained to be strictly positive. #> Warning: The parameter lambda has no priors. This means either no prior is diff --git a/docs/reference/model-method-compile.html b/docs/reference/model-method-compile.html index 63b0fa51..2ae3d9c3 100644 --- a/docs/reference/model-method-compile.html +++ b/docs/reference/model-method-compile.html @@ -31,7 +31,7 @@ cmdstanr - 0.6.0 + 0.7.0
    @@ -148,6 +148,7 @@

    Compile a Stan program

    compile_model_methods = FALSE, compile_hessian_method = FALSE, compile_standalone = FALSE, + dry_run = FALSE, threads = FALSE )
    @@ -227,6 +228,11 @@

    Arguments

    $expose_functions() method.

    +
    dry_run
    +

    (logical) If TRUE, the code will do all checks before compilation, +but skip the actual C++ compilation. Used to speedup tests.

    + +
    threads

    Deprecated and will be removed in a future release. Please turn on threading via cpp_options = list(stan_threads = TRUE) instead.

    @@ -261,7 +267,9 @@

    See also

    model-method-expose_functions, model-method-format, model-method-generate-quantities, +model-method-laplace, model-method-optimize, +model-method-pathfinder, model-method-sample_mpi, model-method-sample, model-method-variables, @@ -278,12 +286,12 @@

    Examples

    mod <- cmdstan_model(file, compile = FALSE) mod$compile() mod$exe_file() -#> [1] "/Users/jgabry/.cmdstan/cmdstan-2.32.2/examples/bernoulli/bernoulli" +#> [1] "/Users/jgabry/.cmdstan/cmdstan-2.33.1/examples/bernoulli/bernoulli" # turn on threading support (for using functions that support within-chain parallelization) mod$compile(force_recompile = TRUE, cpp_options = list(stan_threads = TRUE)) mod$exe_file() -#> [1] "/Users/jgabry/.cmdstan/cmdstan-2.32.2/examples/bernoulli/bernoulli" +#> [1] "/Users/jgabry/.cmdstan/cmdstan-2.33.1/examples/bernoulli/bernoulli" # turn on pedantic mode (new in Stan v2.24) file_pedantic <- write_stan_file(" @@ -295,7 +303,7 @@

    Examples

    } ") mod <- cmdstan_model(file_pedantic, pedantic = TRUE) -#> Warning in '/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpFBtN6X/model-1621885c67f.stan', line 6, column 2: Parameter +#> Warning in '/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/model-11d996d1c906f.stan', line 6, column 2: Parameter #> sigma is given a exponential distribution, which has strictly positive #> support, but sigma was not constrained to be strictly positive. diff --git a/docs/reference/model-method-diagnose.html b/docs/reference/model-method-diagnose.html index 548b5959..e4120b50 100644 --- a/docs/reference/model-method-diagnose.html +++ b/docs/reference/model-method-diagnose.html @@ -21,7 +21,7 @@ cmdstanr - 0.6.0 + 0.7.0 @@ -229,7 +229,9 @@

    See also

    model-method-expose_functions, model-method-format, model-method-generate-quantities, +model-method-laplace, model-method-optimize, +model-method-pathfinder, model-method-sample_mpi, model-method-sample, model-method-variables, @@ -243,11 +245,11 @@

    Examples

    # retrieve the gradients test$gradients() -#> param_idx value model finite_diff error -#> 1 0 -1.310990 28.37700 28.37700 -1.92695e-08 -#> 2 1 -1.931430 8.55341 8.55341 -1.60934e-08 -#> 3 2 0.751196 -12.17180 -12.17180 -2.15070e-08 -#> 4 3 -1.808550 25.15020 25.15020 6.91254e-09 +#> param_idx value model finite_diff error +#> 1 0 0.466346 3.5478 3.5478 1.64210e-08 +#> 2 1 1.496850 -35.5646 -35.5646 6.49428e-09 +#> 3 2 1.385000 -27.1027 -27.1027 -1.36431e-08 +#> 4 3 -0.467569 14.0233 14.0233 1.05492e-08 # } diff --git a/docs/reference/model-method-expose_functions.html b/docs/reference/model-method-expose_functions.html index ad674e0e..c4f4d0d1 100644 --- a/docs/reference/model-method-expose_functions.html +++ b/docs/reference/model-method-expose_functions.html @@ -25,7 +25,7 @@ cmdstanr - 0.6.0 + 0.7.0 @@ -153,7 +153,9 @@

    See also

    model-method-diagnose, model-method-format, model-method-generate-quantities, +model-method-laplace, model-method-optimize, +model-method-pathfinder, model-method-sample_mpi, model-method-sample, model-method-variables, diff --git a/docs/reference/model-method-format.html b/docs/reference/model-method-format.html index 04177977..b0a71308 100644 --- a/docs/reference/model-method-format.html +++ b/docs/reference/model-method-format.html @@ -19,7 +19,7 @@ cmdstanr - 0.6.0 + 0.7.0 @@ -174,7 +174,9 @@

    See also

    model-method-diagnose, model-method-expose_functions, model-method-generate-quantities, +model-method-laplace, model-method-optimize, +model-method-pathfinder, model-method-sample_mpi, model-method-sample, model-method-variables, @@ -184,10 +186,41 @@

    See also

    Examples

    # \dontrun{
+
+# Example of fixing old syntax
+# real x[2] --> array[2] real x;
+file <- write_stan_file("
+parameters {
+  real x[2];
+}
+model {
+  x ~ std_normal();
+}
+")
+
+# set compile=FALSE then call format to fix old syntax
+mod <- cmdstan_model(file, compile = FALSE)
+mod$format(canonicalize = list("deprecations"))
+#> parameters {
+#>   array[2] real x;
+#> }
+#> model {
+#>   x ~ std_normal();
+#> }
+#> 
+#> 
+
+# overwrite the original file instead of just printing it
+mod$format(canonicalize = list("deprecations"), overwrite_file = TRUE)
+#> Old version of the model stored to /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/model_1fc88c86300d78667dd3e476a636c279.stan.bak-20231108103429.
+mod$compile()
+
+
+# Example of removing unnecessary whitespace
 file <- write_stan_file("
 data {
   int N;
-  int y[N];
+  array[N] int y;
 }
 parameters {
   real                     lambda;
diff --git a/docs/reference/model-method-generate-quantities.html b/docs/reference/model-method-generate-quantities.html
index 63e57c29..80a23de2 100644
--- a/docs/reference/model-method-generate-quantities.html
+++ b/docs/reference/model-method-generate-quantities.html
@@ -19,7 +19,7 @@
       
       
         cmdstanr
-        0.6.0
+        0.7.0
    @@ -243,7 +243,9 @@

    See also

    model-method-diagnose, model-method-expose_functions, model-method-format, +model-method-laplace, model-method-optimize, +model-method-pathfinder, model-method-sample_mpi, model-method-sample, model-method-variables, @@ -257,7 +259,7 @@

    Examples

    mcmc_program <- write_stan_file( "data { int<lower=0> N; - int<lower=0,upper=1> y[N]; + array[N] int<lower=0,upper=1> y; } parameters { real<lower=0,upper=1> theta; @@ -287,13 +289,13 @@

    Examples

    gq_program <- write_stan_file( "data { int<lower=0> N; - int<lower=0,upper=1> y[N]; + array[N] int<lower=0,upper=1> y; } parameters { real<lower=0,upper=1> theta; } generated quantities { - int y_rep[N] = bernoulli_rng(rep_vector(theta, N)); + array[N] int y_rep = bernoulli_rng(rep_vector(theta, N)); }" ) diff --git a/docs/reference/model-method-laplace-1.png b/docs/reference/model-method-laplace-1.png new file mode 100644 index 0000000000000000000000000000000000000000..01b1f50d56685003de39ebee8085e0410e301791 GIT binary patch literal 12841 zcmeHNX;f3!zCVhHEh@HFabTz#Kq`ZR35HQ?1p_8jsWKK&5RfUF0AUCa>$TR3APIsD zAzB2=1w>{egkVwO2m~2qiUgF9fDD0z!7wE6oN(8BpYClxyy>p?tVIs%?6dd&55NDo z&&_XfcAGxk^(g=Vn=mK8IRgO7kpQ4{VZ(ap2{-k@G;~{k!NKku=nh@d<2CWn?UTzV zU5Nmoc?14ilQc4O5dgjfFy9>eE;4Ik!0{)+!m-AgHc?b|A01nj8BE2ZGS3BGcaeLu zeyZ58{h8CY3cZ-e8-8GI3od85Jl0yr#cuf`sh`&2fM>;Y`V?&VIsSErU-`{j*z!Jb ziqj&IJPtY0Vv6aflNoI-`7SE}__(K4l44`tJ)WwSVAGOyiEwr+ z0MsAa61nDn*Il9Hjr85JU{n)-F?x?zP4@EQs3tU7FS%Y7$Q9_fWqawUYwu$DmWyhW zCnkk`?H14|?yIZnz|ZsnOg1@k_UG0q4MfaRjx#in<)KvuD+u`3en=U<@7I}D5S8+I zq(UC{pVY#Sf!uYT@Qre2tucH*st+v)UH4O66jx4s(b8R`u? ze3Yt9CR(1ZUy`I%2Buh=Mk_(m@PUL@Tgc%xx9Lf{roOt-tS=e0!u#RMsu^$6>ErFo!2Z=>nt6y5H$rg*>jLnatq1QxzgK7hf!U1}V z4e?TpT=0aMMwYzsUHLYz-5na{>689fOO39;S;)|nr?hm_sdQE?VWi(Onsa)7JJEW1 zvJERYkKOk0EN&1RTY$KR_ZSO^6d;|y-X)y^^n`n|&kB_P2; zNkm`a-{mUu0&bJ$zeBAAYquh1P+fRnVLRF)y74okBrQlCK>f^?$ai*^rTxq8*ZBBq zY4P+plrllr5!71aCvWSJ#7$fzTP$UgQWGmB|+gk+oenet9{0$gV2 zUx$czJTj%pvvDpY2Oqj<718QPS|~5~6xvd4v{(=)I8~^oz5hJPt3_<(U#7wGp_Q;x z8s;ovF0+jOXdr;{OmEt)coQ2!s2)2j9Kt%vRge9+au^oHk(qU#Rozp2+giuTakGgz zKh?lb6A;s7m3jI#w@z%1TkM#ZW!23_6D0Ga+u*^hbMCu2Ut+d92~3-z(8PUzN%3^; zUv>MRsObP4LFe(44;Z(IcC&`}_uxlz$>+^?z@@C#Ita8Bl?&PBS+qh|;fGN}J0{sW zK75~a0Pg$@9fP_?K%}>pMVg1DS@a9P9{fcfw=H*d>@&q!g!5|tJpXF49dd6&&%}3- zFvfnj*HZ~Ms{o$i!e-hD@qspvOnJy`>OQQrMxBvqjgE%;N?b=MeH!XI;^N=$DtKo{ zl0bD=uy~QhzkSUT1-CRb;nEa$muLYlY3*cPC?Bt;nt5uo)&i96f7IaqV9D{%`dM&Y z2vFq6L3uBe93tz51n8}+j!0)WI@aGbB*N17KgESkxL(k9D@)=c*jQ_Z4sd+}|A z*G`rSF!Tg!wgB}OrUN$Ck@|(@&81E#w2R*0lVsmTnHxZ%f&Ugs)PGaAeAR;vJ!x@@ zn-o_{SvjQO!~WU%sEy`BmsRz_; zOL`}!NK^~OaYg38N7r`MSiHxJv!9}D(9zFNN2D_6CfiQo6Cm#>LO}r=pxmCHr=g{< z_eMp{rsfo>NxU#s=3gt-UxC9l&>LW-=bs9is@p=1$L;~Xz|#o!y8B?$0W>c8r%OwT z>9(R?{`6%i+1}B3yc>3_fa0uX*)fKs?c3vePh~ku^yr~7rs{FO#35_9htX5rD4>RezyiHM}0k)@Bkhv;Qcmv*;a?Zlqf~}zm;tyv&WQSr4 z1`@0ZmLy%NM`sk^Vx^#|!fn!!F~=_oI^48Z%PN^X`QTy1nGDX_`Va4S{b8DOuV6z=}yd|#qvo4-vx!DE4l1eka@H)A;mZNr`@-cJHpm_8(yKUW@*1K z=z=|ULf@FlC&jW8*lb={;)yp;^7UY1E3$qd0fB_c)Y6pJ-5I!!!4z<8Z4`Vm37o58nzJ=9yy_L!+&jNcgTRP~e8Ytm zKPjQ}7;wZwq3j)xx0Xwq^&Yhm?QVQzRj9Qg2KXGrr=FD5Z{h9gl?fv!z?XB_{85qW zf}TD$9~ko3I5W6O0UVcv--XyeNbyFd7h*HTESM) z{b4;?MzFq4wHZB^k@2uQg+X>+X<$b?pgY2}hL%^Mdaiddxy#=gojaznqJhpP51%xt zG1`aqUz6I1{KHtb&D;!2=jd@i$!v(|kzGb&yW_x};X$eM$Zo8Vaz0Oi7Z;yIdX`sr zGWqG@-4{<^MD<;XiIpkg4dOe(w(mHnkR1h?*DW3CG{V^aSnnD9wlN&hu3*{YasQP-!;X5!1~AI`WVBg#(C}sZU(2rQNOPv zY(3@t>U1gs2@2-=Dz3z$kdV+svd3fVfQAzaV_t4bE#-@E!+LvQ=R`|^z`jwn=XZC7 z_^SO(*bV@nvEXk3AW|u*yF$zrL}wMur|tvs6Hi!mW$g!Vjdph><9HM%>kG#th_BiRjjBSw(99e2M13Es#;CQG*@ z(EQ~YbGinGfayf{+pex%=u+&;jynDU(%dC;2b;mz!|3IP-~e;G-K%QzzI#Atr{?>b zE8#y@MQg@hDjDGU71r2_JgA|~_kfAt6Vs~q3whPS4Kptu%TIMz41L^-(GU-lVIzST>q;%~GT05a=J>W~LYAODREq z7xDI7(i{FK5c*O2BQr|Gp^1;I^Svpvve$>7_uombeBrTK$nn@*=!{9UoX#Af<`FAy zn?|bKM;PNkrwmT%J!)_vQqM`u$}69BqP6A{F!3q}Cc_ zPeK>DCPM7e>ALt8^Osc1{sQT-&eMwpbPo?f8CqunhCj84Q78p*-~iTttdK?xeN>3_ zGS(qt8^`iFBOf}zcPSb{khYP`+ODn?%igfm6TwA9W>Es4mv?Q}$-&m%tmh%$IFnO$ zFO)EO5@b~he4*_=2wtUFde|qTG;>R+4)$dax-|mL?Y-y;Z~8n=n=@g$!_Pb|XFf7D zf7^Hz8Ra&{mxY;jhNZ^YgY$@_T)`4VPJXw?cT~gUE22JXIB~$T(&~TR@9#8j zHZj6mGzCqEa+yS-rd^U&Ebm>z4V8~3K#_a3kwwHrQ+tjbk>8Y@Hw+X7W}-q~br-u! 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    +
    + + + +
    +
    +
    +

    Run Stan's laplace algorithm

    + Source: R/model.R + +
    + +
    +

    The $laplace() method of a CmdStanModel object produces a +sample from a normal approximation centered at the mode of a distribution +in the unconstrained space. If the mode is a maximum a posteriori (MAP) +estimate, the samples provide an estimate of the mean and standard +deviation of the posterior distribution. If the mode is a maximum +likelihood estimate (MLE), the sample provides an estimate of the standard +error of the likelihood. Whether the mode is the MAP or MLE depends on +the value of the jacobian argument when running optimization. See the +CmdStan User’s Guide +for more details.

    +

    Any argument left as NULL will default to the default value used by the +installed version of CmdStan.

    +
    + +
    + 
    laplace(
+  data = NULL,
+  seed = NULL,
+  refresh = NULL,
+  init = NULL,
+  save_latent_dynamics = FALSE,
+  output_dir = NULL,
+  output_basename = NULL,
+  sig_figs = NULL,
+  threads = NULL,
+  opencl_ids = NULL,
+  mode = NULL,
+  opt_args = NULL,
+  jacobian = TRUE,
+  draws = NULL
+)
    +
    + +
    +

    Arguments

    +
    data
    +

    (multiple options) The data to use for the variables specified in +the data block of the Stan program. One of the following:

    • A named list of R objects with the names corresponding to variables +declared in the data block of the Stan program. Internally this list is then +written to JSON for CmdStan using write_stan_json(). See +write_stan_json() for details on the conversions performed on R objects +before they are passed to Stan.

      • +

    • A path to a data file compatible with CmdStan (JSON or R dump). See the +appendices in the CmdStan guide for details on using these formats.

      • +

    • NULL or an empty list if the Stan program has no data block.

      • +
    + + +
    seed
    +

    (positive integer(s)) A seed for the (P)RNG to pass to CmdStan. +In the case of multi-chain sampling the single seed will automatically be +augmented by the the run (chain) ID so that each chain uses a different +seed. The exception is the transformed data block, which defaults to +using same seed for all chains so that the same data is generated for all +chains if RNG functions are used. The only time seed should be specified +as a vector (one element per chain) is if RNG functions are used in +transformed data and the goal is to generate different data for each +chain.

    + + +
    refresh
    +

    (non-negative integer) The number of iterations between +printed screen updates. If refresh = 0, only error messages will be +printed.

    + + +
    init
    +

    (multiple options) The initialization method to use for the +variables declared in the parameters block of the Stan program. One of +the following:

    • A real number x>0. This initializes all parameters randomly between +[-x,x] on the unconstrained parameter space.;

      • +

    • The number 0. This initializes all parameters to 0;

      • +

    • A character vector of paths (one per chain) to JSON or Rdump files +containing initial values for all or some parameters. See +write_stan_json() to write R objects to JSON files compatible with +CmdStan.

      • +

    • A list of lists containing initial values for all or some parameters. For +MCMC the list should contain a sublist for each chain. For optimization and +variational inference there should be just one sublist. The sublists should +have named elements corresponding to the parameters for which you are +specifying initial values. See Examples.

      • +

    • A function that returns a single list with names corresponding to the +parameters for which you are specifying initial values. The function can +take no arguments or a single argument chain_id. For MCMC, if the function +has argument chain_id it will be supplied with the chain id (from 1 to +number of chains) when called to generate the initial values. See +Examples.

      • +
    + + +
    save_latent_dynamics
    +

    Ignored for this method.

    + + +
    output_dir
    +

    (string) A path to a directory where CmdStan should write +its output CSV files. For interactive use this can typically be left at +NULL (temporary directory) since CmdStanR makes the CmdStan output +(posterior draws and diagnostics) available in R via methods of the fitted +model objects. The behavior of output_dir is as follows:

    • If NULL (the default), then the CSV files are written to a temporary +directory and only saved permanently if the user calls one of the $save_* +methods of the fitted model object (e.g., +$save_output_files()). These temporary +files are removed when the fitted model object is +garbage collected (manually or automatically).

      • +

    • If a path, then the files are created in output_dir with names +corresponding to the defaults used by $save_output_files().

      • +
    + + +
    output_basename
    +

    (string) A string to use as a prefix for the names of +the output CSV files of CmdStan. If NULL (the default), the basename of +the output CSV files will be comprised from the model name, timestamp, and +5 random characters.

    + + +
    sig_figs
    +

    (positive integer) The number of significant figures used +when storing the output values. By default, CmdStan represent the output +values with 6 significant figures. The upper limit for sig_figs is 18. +Increasing this value will result in larger output CSV files and thus an +increased usage of disk space.

    + + +
    threads
    +

    (positive integer) If the model was +compiled with threading support, the number of +threads to use in parallelized sections (e.g., when +using the Stan functions reduce_sum() or map_rect()).

    + + +
    opencl_ids
    +

    (integer vector of length 2) The platform and +device IDs of the OpenCL device to use for fitting. The model must +be compiled with cpp_options = list(stan_opencl = TRUE) for this +argument to have an effect.

    + + +
    mode
    +

    (multiple options) The mode to center the approximation at. One +of the following:

    • A CmdStanMLE object from a previous run of $optimize().

      • +

    • The path to a CmdStan CSV file from running optimization.

      • +

    • NULL, in which case $optimize() will be run +with jacobian=jacobian (see the jacobian argument below).

      • +

    In all cases the total time reported by $time() will be +the time of the Laplace sampling step only and does not include the time +taken to run the $optimize() method.

    + + +
    opt_args
    +

    (named list) A named list of optional arguments to pass to +$optimize() if mode=NULL.

    + + +
    jacobian
    +

    (logical) Whether or not to enable the Jacobian adjustment +for constrained parameters. The default is TRUE. See the +Laplace Sampling +section of the CmdStan User's Guide for more details. If mode is not +NULL then the value of jacobian must match the value used when +optimization was originally run. If mode is NULL then the value of +jacobian specified here is used when running optimization.

    + + +
    draws
    +

    (positive integer) The number of draws to take.

    + +
    +
    +

    Value

    + + +

    A CmdStanLaplace object.

    +
    + + +
    +

    Examples

    + 
    # \dontrun{
+file <- file.path(cmdstan_path(), "examples/bernoulli/bernoulli.stan")
+mod <- cmdstan_model(file)
+mod$print()
+#> data {
+#>   int<lower=0> N;
+#>   array[N] int<lower=0,upper=1> y;
+#> }
+#> parameters {
+#>   real<lower=0,upper=1> theta;
+#> }
+#> model {
+#>   theta ~ beta(1,1);  // uniform prior on interval 0,1
+#>   y ~ bernoulli(theta);
+#> }
+
+stan_data <- list(N = 10, y = c(0,1,0,0,0,0,0,0,0,1))
+fit_mode <- mod$optimize(data = stan_data, jacobian = TRUE)
+#> Initial log joint probability = -6.78622 
+#>     Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes  
+#>        4      -6.74802   0.000186334   5.91326e-07           1           1        7    
+#> Optimization terminated normally:  
+#>   Convergence detected: relative gradient magnitude is below tolerance 
+#> Finished in  0.1 seconds.
+fit_laplace <- mod$laplace(data = stan_data, mode = fit_mode)
+#> Calculating Hessian 
+#> Calculating inverse of Cholesky factor 
+#> Generating draws 
+#> iteration: 0 
+#> iteration: 100 
+#> iteration: 200 
+#> iteration: 300 
+#> iteration: 400 
+#> iteration: 500 
+#> iteration: 600 
+#> iteration: 700 
+#> iteration: 800 
+#> iteration: 900 
+#> Finished in  0.1 seconds.
+fit_laplace$summary()
+#> # A tibble: 3 × 7
+#>   variable      mean median    sd   mad      q5      q95
+#>   <chr>        <dbl>  <dbl> <dbl> <dbl>   <dbl>    <dbl>
+#> 1 lp__        -7.24  -6.98  0.677 0.309 -8.74   -6.75   
+#> 2 lp_approx__ -0.505 -0.230 0.686 0.308 -1.92   -0.00197
+#> 3 theta        0.269  0.254 0.123 0.125  0.0982  0.500  
+
+# if mode isn't specified optimize is run internally first
+fit_laplace <- mod$laplace(data = stan_data)
+#> Initial log joint probability = -6.7528 
+#>     Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes  
+#>        3      -6.74802   0.000769281   2.15262e-05           1           1        6    
+#> Optimization terminated normally:  
+#>   Convergence detected: relative gradient magnitude is below tolerance 
+#> Finished in  0.1 seconds.
+#> Calculating Hessian 
+#> Calculating inverse of Cholesky factor 
+#> Generating draws 
+#> iteration: 0 
+#> iteration: 100 
+#> iteration: 200 
+#> iteration: 300 
+#> iteration: 400 
+#> iteration: 500 
+#> iteration: 600 
+#> iteration: 700 
+#> iteration: 800 
+#> iteration: 900 
+#> Finished in  0.1 seconds.
+fit_laplace$summary()
+#> # A tibble: 3 × 7
+#>   variable      mean median    sd   mad      q5      q95
+#>   <chr>        <dbl>  <dbl> <dbl> <dbl>   <dbl>    <dbl>
+#> 1 lp__        -7.26  -6.95  0.755 0.289 -8.76   -6.75   
+#> 2 lp_approx__ -0.518 -0.203 0.755 0.285 -2.15   -0.00170
+#> 3 theta        0.267  0.250 0.125 0.115  0.0947  0.505  
+
+# plot approximate posterior
+bayesplot::mcmc_hist(fit_laplace$draws("theta"))
+#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
+
+# }
+
+
+
    +
    +
    + +
    + + +
    +

    Developed by Jonah Gabry, Rok Češnovar, Andrew Johnson.

    +
    + +
    +

    Site built with pkgdown 2.0.7.

    +
    + +
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b/docs/reference/model-method-optimize.html @@ -1,11 +1,14 @@ Run Stan's optimization algorithms — model-method-optimize • cmdstanr @@ -23,7 +26,7 @@ cmdstanr - 0.6.0 + 0.7.0
    @@ -111,12 +114,15 @@

    Run Stan's optimization algorithms

    The $optimize() method of a CmdStanModel object runs -Stan's optimizer to obtain a posterior mode (penalized maximum likelihood) -estimate.

    -

    Any argument left as NULL will default to the default value used by the -installed version of CmdStan. See the -CmdStan User’s Guide +Stan's optimizer to obtain a (penalized) maximum likelihood estimate or a +maximum a posteriori estimate (if jacobian=TRUE). See the +CmdStan User's Guide for more details.

    +

    Any argument left as NULL will default to the default value used by the +installed version of CmdStan. See the CmdStan User’s Guide for more details on the +default arguments. The default values can also be obtained by checking the +metadata of an example model, e.g., +cmdstanr_example(method="optimize")$metadata().

    @@ -132,6 +138,7 @@

    Run Stan's optimization algorithms

    threads = NULL, opencl_ids = NULL, algorithm = NULL, + jacobian = FALSE, init_alpha = NULL, iter = NULL, tol_obj = NULL, @@ -265,6 +272,17 @@

    Arguments

    running cmdstanr_example(method="optimize")$metadata().

    +
    jacobian
    +

    (logical) Whether or not to use the Jacobian adjustment for +constrained variables. For historical reasons, the default is FALSE, meaning optimization +yields the (regularized) maximum likelihood estimate. Setting it to TRUE +yields the maximum a posteriori estimate. See the +Maximum Likelihood Estimation +section of the CmdStan User's Guide for more details. +For use later with $laplace() the jacobian +argument should typically be set to TRUE.

    + +
    init_alpha

    (positive real) The initial step size parameter.

    @@ -303,16 +321,6 @@

    Value

    A CmdStanMLE object.

    -
    -
    -

    Details

    -

    CmdStan can find the posterior mode (assuming there is one). If the -posterior is not convex, there is no guarantee Stan will be able to find -the global mode as opposed to a local optimum of log probability. For -optimization, the mode is calculated without the Jacobian adjustment for -constrained variables, which shifts the mode due to the change of -variables. Thus modes correspond to modes of the model as written.

    -

    -- CmdStan User's Guide

    See also

    @@ -328,6 +336,8 @@

    See also

    model-method-expose_functions, model-method-format, model-method-generate-quantities, +model-method-laplace, +model-method-pathfinder, model-method-sample_mpi, model-method-sample, model-method-variables, @@ -348,7 +358,7 @@

    Examples

    # Otherwise use the `path` argument to specify the location of your # CmdStan installation.) set_cmdstan_path(path = NULL) -#> CmdStan path set to: /Users/jgabry/.cmdstan/cmdstan-2.32.2 +#> CmdStan path set to: /Users/jgabry/.cmdstan/cmdstan-2.33.1 # Create a CmdStanModel object from a Stan program, # here using the example model that comes with CmdStan @@ -435,10 +445,22 @@

    Examples

    fit_mcmc$summary() #> # A tibble: 2 × 10 #> variable mean median sd mad q5 q95 rhat ess_bulk ess_tail -#> <chr> <num> <num> <num> <num> <num> <num> <num> <num> <num> +#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 lp__ -7.30 -7.03 0.721 0.380 -8.82 -6.75 1.00 902. 1006. #> 2 theta 0.247 0.233 0.122 0.129 0.0786 0.470 1.00 762. 712. +# Check sampling diagnostics +fit_mcmc$diagnostic_summary() +#> $num_divergent +#> [1] 0 0 +#> +#> $num_max_treedepth +#> [1] 0 0 +#> +#> $ebfmi +#> [1] 1.017555 1.250490 +#> + # Get posterior draws draws <- fit_mcmc$draws() print(draws) @@ -487,51 +509,8 @@

    Examples

    #> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`. -# Call CmdStan's diagnose and stansummary utilities -fit_mcmc$cmdstan_diagnose() -#> Processing csv files: /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpFBtN6X/bernoulli-202307251438-1-65b170.csv, /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpFBtN6X/bernoulli-202307251438-2-65b170.csv -#> -#> Checking sampler transitions treedepth. -#> Treedepth satisfactory for all transitions. -#> -#> Checking sampler transitions for divergences. -#> No divergent transitions found. -#> -#> Checking E-BFMI - sampler transitions HMC potential energy. -#> E-BFMI satisfactory. -#> -#> Effective sample size satisfactory. -#> -#> Split R-hat values satisfactory all parameters. -#> -#> Processing complete, no problems detected. -fit_mcmc$cmdstan_summary() -#> Inference for Stan model: bernoulli_model -#> 2 chains: each with iter=(1000,1000); warmup=(0,0); thin=(1,1); 2000 iterations saved. -#> -#> Warmup took (0.0040, 0.0040) seconds, 0.0080 seconds total -#> Sampling took (0.011, 0.011) seconds, 0.022 seconds total -#> -#> Mean MCSE StdDev 5% 50% 95% N_Eff N_Eff/s R_hat -#> -#> lp__ -7.3 2.6e-02 0.72 -8.8 -7.0 -6.8 781 35502 1.0 -#> accept_stat__ 0.92 8.3e-03 0.13 0.64 0.97 1.0 235 10662 1.0e+00 -#> stepsize__ 0.95 7.9e-02 0.079 0.87 1.0 1.0 1.0 46 2.0e+13 -#> treedepth__ 1.4 1.1e-02 0.48 1.0 1.0 2.0 1874 85179 1.0e+00 -#> n_leapfrog__ 2.5 1.4e-01 1.3 1.0 3.0 3.0 89 4050 1.0e+00 -#> divergent__ 0.00 nan 0.00 0.00 0.00 0.00 nan nan nan -#> energy__ 7.8 3.6e-02 1.00 6.8 7.5 9.6 775 35215 1.0e+00 -#> -#> theta 0.25 4.3e-03 0.12 0.079 0.23 0.47 796 36197 1.0 -#> -#> Samples were drawn using hmc with nuts. -#> For each parameter, N_Eff is a crude measure of effective sample size, -#> and R_hat is the potential scale reduction factor on split chains (at -#> convergence, R_hat=1). - # For models fit using MCMC, if you like working with RStan's stanfit objects # then you can create one with rstan::read_stan_csv() - # stanfit <- rstan::read_stan_csv(fit_mcmc$output_files()) @@ -545,16 +524,56 @@

    Examples

    #> Optimization terminated normally: #> Convergence detected: relative gradient magnitude is below tolerance #> Finished in 0.1 seconds. - fit_optim$summary() #> # A tibble: 2 × 2 #> variable estimate -#> <chr> <num> +#> <chr> <dbl> #> 1 lp__ -5.00 #> 2 theta 0.2 +# Run 'optimize' again with 'jacobian=TRUE' and then draw from laplace approximation +# to the posterior +fit_optim <- mod$optimize(data = my_data_file, jacobian = TRUE) +#> Initial log joint probability = -11.84 +#> Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes +#> 5 -6.74802 0.00117606 2.01092e-05 1 1 8 +#> Optimization terminated normally: +#> Convergence detected: relative gradient magnitude is below tolerance +#> Finished in 0.1 seconds. +fit_laplace <- mod$laplace(data = my_data_file, mode = fit_optim, draws = 2000) +#> Calculating Hessian +#> Calculating inverse of Cholesky factor +#> Generating draws +#> iteration: 0 +#> iteration: 100 +#> iteration: 200 +#> iteration: 300 +#> iteration: 400 +#> iteration: 500 +#> iteration: 600 +#> iteration: 700 +#> iteration: 800 +#> iteration: 900 +#> iteration: 1000 +#> iteration: 1100 +#> iteration: 1200 +#> iteration: 1300 +#> iteration: 1400 +#> iteration: 1500 +#> iteration: 1600 +#> iteration: 1700 +#> iteration: 1800 +#> iteration: 1900 +#> Finished in 0.1 seconds. +fit_laplace$summary() +#> # A tibble: 3 × 7 +#> variable mean median sd mad q5 q95 +#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 lp__ -7.23 -6.96 0.703 0.291 -8.51 -6.75 +#> 2 lp_approx__ -0.490 -0.218 0.693 0.294 -1.85 -0.00308 +#> 3 theta 0.268 0.247 0.122 0.119 0.0992 0.495 -# Run 'variational' method to approximate the posterior (default is meanfield ADVI) +# Run 'variational' method to use ADVI to approximate posterior fit_vb <- mod$variational(data = stan_data, seed = 123) #> ------------------------------------------------------------ #> EXPERIMENTAL ALGORITHM: @@ -579,20 +598,47 @@

    Examples

    #> Drawing a sample of size 1000 from the approximate posterior... #> COMPLETED. #> Finished in 0.1 seconds. - fit_vb$summary() #> # A tibble: 3 × 7 #> variable mean median sd mad q5 q95 -#> <chr> <num> <num> <num> <num> <num> <num> +#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 lp__ -7.18 -6.94 0.588 0.259 -8.36 -6.75 #> 2 lp_approx__ -0.515 -0.221 0.692 0.303 -2.06 -0.00257 #> 3 theta 0.263 0.246 0.115 0.113 0.106 0.481 - -# Plot approximate posterior using bayesplot mcmc_hist(fit_vb$draws("theta")) #> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`. +# Run 'pathfinder' method, a new alternative to the variational method +fit_pf <- mod$pathfinder(data = stan_data, seed = 123) +#> Path [1] :Initial log joint density = -11.008832 +#> Path [1] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes +#> 5 -6.748e+00 9.383e-04 1.391e-05 1.000e+00 1.000e+00 126 -6.264e+00 -6.264e+00 +#> Path [1] :Best Iter: [3] ELBO (-6.195408) evaluations: (126) +#> Path [2] :Initial log joint density = -7.318450 +#> Path [2] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes +#> 4 -6.748e+00 5.414e-03 1.618e-04 1.000e+00 1.000e+00 101 -6.251e+00 -6.251e+00 +#> Path [2] :Best Iter: [3] ELBO (-6.229174) evaluations: (101) +#> Path [3] :Initial log joint density = -12.374612 +#> Path [3] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes +#> 5 -6.748e+00 1.419e-03 2.837e-05 1.000e+00 1.000e+00 126 -6.199e+00 -6.199e+00 +#> Path [3] :Best Iter: [5] ELBO (-6.199185) evaluations: (126) +#> Path [4] :Initial log joint density = -13.009824 +#> Path [4] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes +#> 5 -6.748e+00 1.677e-03 3.885e-05 1.000e+00 1.000e+00 126 -6.173e+00 -6.173e+00 +#> Path [4] :Best Iter: [5] ELBO (-6.172860) evaluations: (126) +#> Total log probability function evaluations:4379 +#> Finished in 0.1 seconds. +fit_pf$summary() +#> # A tibble: 3 × 7 +#> variable mean median sd mad q5 q95 +#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 lp_approx__ -1.08 -0.728 0.886 0.304 -2.71 -0.511 +#> 2 lp__ -7.26 -6.96 0.738 0.297 -8.72 -6.75 +#> 3 theta 0.249 0.230 0.120 0.121 0.0854 0.471 +mcmc_hist(fit_pf$draws("theta")) +#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`. + # Specifying initial values as a function fit_mcmc_w_init_fun <- mod$sample( diff --git a/docs/reference/model-method-pathfinder-1.png b/docs/reference/model-method-pathfinder-1.png new file mode 100644 index 0000000000000000000000000000000000000000..0aa41c0e91b00f182931ce9cafb34ba5eb0c92d0 GIT binary patch literal 12728 zcmeHt2~<v3Dx@%Znulp|GlAL?a-DmG_ z?{DvY@BMQq%dMMtZ-&8OTajl@+QDG5kuaFd)z3D9ZwAvI&4JU#tJan$!5JK)6E#?H z+7xugF$4xvxd;8OOCFo|hQa;CKdl9(2k`|NH2bmL~@jHkjFpn(9<(!4YPL z#iwndq3dP2jpLJWtni)W3EV+AUmkZXot~Xda+8j{ zw(f(LfLU##O3(Ql+@N!u-FjW#<;&smK~ceP)AZawsw@MR>l%zvIv z2Yal3z(q%WbVf(7T&g^6%cLiAS_ZL)YK^1hz>MeHBiC(vxS-3e;u^DAq|>_0K|LBp zk*iCcU!3gibOx9Fkl7#~^stCOa>P6Ky*~5ptCiJa&igShU&iF8aS|BsIGf2syrA5U zC@rN!bCT1Gq!JPM8+4x?-f6FDvG!ivGogZ^)RB0#E12%FPSb7Q8H@NL#qq=#rJ>ma zRje0jSb9kJO~FpEz-7x3S%bgL9-yNc{d(iRc~#S6Zx+QDaqh~sdCWx8VE;vauz?Xt z2S-GtxMR)H=>I?KwP0kPQ?6mzewp`vj5AO?gov`N85S)q%KPB*_AHBM%{0hVd@G)9 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z6VhQ3zEo{8(2NirTrjg%ryZ>5yysQ-8%r8!11a7hRiU^sX}b;#&G=9C({8U@?nX3b z_>?8AmM`jhvur||K5LJgdB8VDZ|>_@iy>NJ)a@2pFofmLE7j&Dh`8=Sz7Mfpi>=EJ_L{W;+6&~<^dShy@$bOP2A7>9g4u);PoUl7ydd%b2zX7wgNY?-W literal 0 HcmV?d00001 diff --git a/docs/reference/model-method-pathfinder.html b/docs/reference/model-method-pathfinder.html new file mode 100644 index 00000000..f6423f16 --- /dev/null +++ b/docs/reference/model-method-pathfinder.html @@ -0,0 +1,774 @@ + +Run Stan's Pathfinder Variational Inference Algorithm — model-method-pathfinder • cmdstanr + + +
    +
    + + + +
    +
    +
    +

    Run Stan's Pathfinder Variational Inference Algorithm

    + Source: R/model.R + +
    + +
    +

    The $pathfinder() method of a CmdStanModel object runs +Stan's Pathfinder algorithms. Pathfinder is a variational method for +approximately sampling from differentiable log densities. Starting from a +random initialization, Pathfinder locates normal approximations to the +target density along a quasi-Newton optimization path, with local +covariance estimated using the negative inverse Hessian estimates produced +by the L-BFGS optimizer. Pathfinder returns draws from the Gaussian +approximation with the lowest estimated Kullback-Leibler (KL) divergence to +the true posterior. See the +CmdStan User’s Guide +for more details.

    +

    Any argument left as NULL will default to the default value used by the +installed version of CmdStan

    +
    + +
    + 
    pathfinder(
+  data = NULL,
+  seed = NULL,
+  refresh = NULL,
+  init = NULL,
+  save_latent_dynamics = FALSE,
+  output_dir = NULL,
+  output_basename = NULL,
+  sig_figs = NULL,
+  opencl_ids = NULL,
+  num_threads = NULL,
+  init_alpha = NULL,
+  tol_obj = NULL,
+  tol_rel_obj = NULL,
+  tol_grad = NULL,
+  tol_rel_grad = NULL,
+  tol_param = NULL,
+  history_size = NULL,
+  single_path_draws = NULL,
+  draws = NULL,
+  num_paths = NULL,
+  max_lbfgs_iters = NULL,
+  num_elbo_draws = NULL,
+  save_single_paths = NULL
+)
    +
    + +
    +

    Arguments

    +
    data
    +

    (multiple options) The data to use for the variables specified in +the data block of the Stan program. One of the following:

    • A named list of R objects with the names corresponding to variables +declared in the data block of the Stan program. Internally this list is then +written to JSON for CmdStan using write_stan_json(). See +write_stan_json() for details on the conversions performed on R objects +before they are passed to Stan.

      • +

    • A path to a data file compatible with CmdStan (JSON or R dump). See the +appendices in the CmdStan guide for details on using these formats.

      • +

    • NULL or an empty list if the Stan program has no data block.

      • +
    + + +
    seed
    +

    (positive integer(s)) A seed for the (P)RNG to pass to CmdStan. +In the case of multi-chain sampling the single seed will automatically be +augmented by the the run (chain) ID so that each chain uses a different +seed. The exception is the transformed data block, which defaults to +using same seed for all chains so that the same data is generated for all +chains if RNG functions are used. The only time seed should be specified +as a vector (one element per chain) is if RNG functions are used in +transformed data and the goal is to generate different data for each +chain.

    + + +
    refresh
    +

    (non-negative integer) The number of iterations between +printed screen updates. If refresh = 0, only error messages will be +printed.

    + + +
    init
    +

    (multiple options) The initialization method to use for the +variables declared in the parameters block of the Stan program. One of +the following:

    • A real number x>0. This initializes all parameters randomly between +[-x,x] on the unconstrained parameter space.;

      • +

    • The number 0. This initializes all parameters to 0;

      • +

    • A character vector of paths (one per chain) to JSON or Rdump files +containing initial values for all or some parameters. See +write_stan_json() to write R objects to JSON files compatible with +CmdStan.

      • +

    • A list of lists containing initial values for all or some parameters. For +MCMC the list should contain a sublist for each chain. For optimization and +variational inference there should be just one sublist. The sublists should +have named elements corresponding to the parameters for which you are +specifying initial values. See Examples.

      • +

    • A function that returns a single list with names corresponding to the +parameters for which you are specifying initial values. The function can +take no arguments or a single argument chain_id. For MCMC, if the function +has argument chain_id it will be supplied with the chain id (from 1 to +number of chains) when called to generate the initial values. See +Examples.

      • +
    + + +
    save_latent_dynamics
    +

    (logical) Should auxiliary diagnostic information +about the latent dynamics be written to temporary diagnostic CSV files? +This argument replaces CmdStan's diagnostic_file argument and the content +written to CSV is controlled by the user's CmdStan installation and not +CmdStanR (for some algorithms no content may be written). The default +is FALSE, which is appropriate for almost every use case. To save the +temporary files created when save_latent_dynamics=TRUE see the +$save_latent_dynamics_files() +method.

    + + +
    output_dir
    +

    (string) A path to a directory where CmdStan should write +its output CSV files. For interactive use this can typically be left at +NULL (temporary directory) since CmdStanR makes the CmdStan output +(posterior draws and diagnostics) available in R via methods of the fitted +model objects. The behavior of output_dir is as follows:

    • If NULL (the default), then the CSV files are written to a temporary +directory and only saved permanently if the user calls one of the $save_* +methods of the fitted model object (e.g., +$save_output_files()). These temporary +files are removed when the fitted model object is +garbage collected (manually or automatically).

      • +

    • If a path, then the files are created in output_dir with names +corresponding to the defaults used by $save_output_files().

      • +
    + + +
    output_basename
    +

    (string) A string to use as a prefix for the names of +the output CSV files of CmdStan. If NULL (the default), the basename of +the output CSV files will be comprised from the model name, timestamp, and +5 random characters.

    + + +
    sig_figs
    +

    (positive integer) The number of significant figures used +when storing the output values. By default, CmdStan represent the output +values with 6 significant figures. The upper limit for sig_figs is 18. +Increasing this value will result in larger output CSV files and thus an +increased usage of disk space.

    + + +
    opencl_ids
    +

    (integer vector of length 2) The platform and +device IDs of the OpenCL device to use for fitting. The model must +be compiled with cpp_options = list(stan_opencl = TRUE) for this +argument to have an effect.

    + + +
    num_threads
    +

    (positive integer) If the model was +compiled with threading support, the number of +threads to use in parallelized sections (e.g., for multi-path pathfinder +as well as reduce_sum).

    + + +
    init_alpha
    +

    (positive real) The initial step size parameter.

    + + +
    tol_obj
    +

    (positive real) Convergence tolerance on changes in objective function value.

    + + +
    tol_rel_obj
    +

    (positive real) Convergence tolerance on relative changes in objective function value.

    + + +
    tol_grad
    +

    (positive real) Convergence tolerance on the norm of the gradient.

    + + +
    tol_rel_grad
    +

    (positive real) Convergence tolerance on the relative norm of the gradient.

    + + +
    tol_param
    +

    (positive real) Convergence tolerance on changes in parameter value.

    + + +
    history_size
    +

    (positive integer) The size of the history used when +approximating the Hessian.

    + + +
    single_path_draws
    +

    (positive integer) Number of draws a single +pathfinder should return. The number of draws PSIS sampling samples from +will be equal to single_path_draws * num_paths.

    + + +
    draws
    +

    (positive integer) Number of draws to return after performing +pareto smooted importance sampling (PSIS). This must be smaller than +single_path_draws * num_paths.

    + + +
    num_paths
    +

    (positive integer) Number of single pathfinders to run.

    + + +
    max_lbfgs_iters
    +

    (positive integer) The maximum number of iterations +for LBFGS.

    + + +
    num_elbo_draws
    +

    (positive integer) Number of draws to make when +calculating the ELBO of the approximation at each iteration of LBFGS.

    + + +
    save_single_paths
    +

    (logical) Whether to save the results of single +pathfinder runs in multi-pathfinder.

    + +
    +
    +

    Value

    + + +

    A CmdStanPathfinder object.

    +
    + + +
    +

    Examples

    + 
    # \dontrun{
+library(cmdstanr)
+library(posterior)
+library(bayesplot)
+color_scheme_set("brightblue")
+
+# Set path to CmdStan
+# (Note: if you installed CmdStan via install_cmdstan() with default settings
+# then setting the path is unnecessary but the default below should still work.
+# Otherwise use the `path` argument to specify the location of your
+# CmdStan installation.)
+set_cmdstan_path(path = NULL)
+#> CmdStan path set to: /Users/jgabry/.cmdstan/cmdstan-2.33.1
+
+# Create a CmdStanModel object from a Stan program,
+# here using the example model that comes with CmdStan
+file <- file.path(cmdstan_path(), "examples/bernoulli/bernoulli.stan")
+mod <- cmdstan_model(file)
+mod$print()
+#> data {
+#>   int<lower=0> N;
+#>   array[N] int<lower=0,upper=1> y;
+#> }
+#> parameters {
+#>   real<lower=0,upper=1> theta;
+#> }
+#> model {
+#>   theta ~ beta(1,1);  // uniform prior on interval 0,1
+#>   y ~ bernoulli(theta);
+#> }
+
+# Data as a named list (like RStan)
+stan_data <- list(N = 10, y = c(0,1,0,0,0,0,0,0,0,1))
+
+# Run MCMC using the 'sample' method
+fit_mcmc <- mod$sample(
+  data = stan_data,
+  seed = 123,
+  chains = 2,
+  parallel_chains = 2
+)
+#> Running MCMC with 2 parallel chains...
+#> 
+#> Chain 1 Iteration:    1 / 2000 [  0%]  (Warmup) 
+#> Chain 1 Iteration:  100 / 2000 [  5%]  (Warmup) 
+#> Chain 1 Iteration:  200 / 2000 [ 10%]  (Warmup) 
+#> Chain 1 Iteration:  300 / 2000 [ 15%]  (Warmup) 
+#> Chain 1 Iteration:  400 / 2000 [ 20%]  (Warmup) 
+#> Chain 1 Iteration:  500 / 2000 [ 25%]  (Warmup) 
+#> Chain 1 Iteration:  600 / 2000 [ 30%]  (Warmup) 
+#> Chain 1 Iteration:  700 / 2000 [ 35%]  (Warmup) 
+#> Chain 1 Iteration:  800 / 2000 [ 40%]  (Warmup) 
+#> Chain 1 Iteration:  900 / 2000 [ 45%]  (Warmup) 
+#> Chain 1 Iteration: 1000 / 2000 [ 50%]  (Warmup) 
+#> Chain 1 Iteration: 1001 / 2000 [ 50%]  (Sampling) 
+#> Chain 1 Iteration: 1100 / 2000 [ 55%]  (Sampling) 
+#> Chain 1 Iteration: 1200 / 2000 [ 60%]  (Sampling) 
+#> Chain 1 Iteration: 1300 / 2000 [ 65%]  (Sampling) 
+#> Chain 1 Iteration: 1400 / 2000 [ 70%]  (Sampling) 
+#> Chain 1 Iteration: 1500 / 2000 [ 75%]  (Sampling) 
+#> Chain 1 Iteration: 1600 / 2000 [ 80%]  (Sampling) 
+#> Chain 1 Iteration: 1700 / 2000 [ 85%]  (Sampling) 
+#> Chain 1 Iteration: 1800 / 2000 [ 90%]  (Sampling) 
+#> Chain 1 Iteration: 1900 / 2000 [ 95%]  (Sampling) 
+#> Chain 1 Iteration: 2000 / 2000 [100%]  (Sampling) 
+#> Chain 2 Iteration:    1 / 2000 [  0%]  (Warmup) 
+#> Chain 2 Iteration:  100 / 2000 [  5%]  (Warmup) 
+#> Chain 2 Iteration:  200 / 2000 [ 10%]  (Warmup) 
+#> Chain 2 Iteration:  300 / 2000 [ 15%]  (Warmup) 
+#> Chain 2 Iteration:  400 / 2000 [ 20%]  (Warmup) 
+#> Chain 2 Iteration:  500 / 2000 [ 25%]  (Warmup) 
+#> Chain 2 Iteration:  600 / 2000 [ 30%]  (Warmup) 
+#> Chain 2 Iteration:  700 / 2000 [ 35%]  (Warmup) 
+#> Chain 2 Iteration:  800 / 2000 [ 40%]  (Warmup) 
+#> Chain 2 Iteration:  900 / 2000 [ 45%]  (Warmup) 
+#> Chain 2 Iteration: 1000 / 2000 [ 50%]  (Warmup) 
+#> Chain 2 Iteration: 1001 / 2000 [ 50%]  (Sampling) 
+#> Chain 2 Iteration: 1100 / 2000 [ 55%]  (Sampling) 
+#> Chain 2 Iteration: 1200 / 2000 [ 60%]  (Sampling) 
+#> Chain 2 Iteration: 1300 / 2000 [ 65%]  (Sampling) 
+#> Chain 2 Iteration: 1400 / 2000 [ 70%]  (Sampling) 
+#> Chain 2 Iteration: 1500 / 2000 [ 75%]  (Sampling) 
+#> Chain 2 Iteration: 1600 / 2000 [ 80%]  (Sampling) 
+#> Chain 2 Iteration: 1700 / 2000 [ 85%]  (Sampling) 
+#> Chain 2 Iteration: 1800 / 2000 [ 90%]  (Sampling) 
+#> Chain 2 Iteration: 1900 / 2000 [ 95%]  (Sampling) 
+#> Chain 2 Iteration: 2000 / 2000 [100%]  (Sampling) 
+#> Chain 1 finished in 0.0 seconds.
+#> Chain 2 finished in 0.0 seconds.
+#> 
+#> Both chains finished successfully.
+#> Mean chain execution time: 0.0 seconds.
+#> Total execution time: 0.2 seconds.
+#> 
+
+# Use 'posterior' package for summaries
+fit_mcmc$summary()
+#> # A tibble: 2 × 10
+#>   variable   mean median    sd   mad      q5    q95  rhat ess_bulk ess_tail
+#>   <chr>     <dbl>  <dbl> <dbl> <dbl>   <dbl>  <dbl> <dbl>    <dbl>    <dbl>
+#> 1 lp__     -7.30  -7.03  0.721 0.380 -8.82   -6.75   1.00     902.    1006.
+#> 2 theta     0.247  0.233 0.122 0.129  0.0786  0.470  1.00     762.     712.
+
+# Check sampling diagnostics
+fit_mcmc$diagnostic_summary()
+#> $num_divergent
+#> [1] 0 0
+#> 
+#> $num_max_treedepth
+#> [1] 0 0
+#> 
+#> $ebfmi
+#> [1] 1.017555 1.250490
+#> 
+
+# Get posterior draws
+draws <- fit_mcmc$draws()
+print(draws)
+#> # A draws_array: 1000 iterations, 2 chains, and 2 variables
+#> , , variable = lp__
+#> 
+#>          chain
+#> iteration    1    2
+#>         1 -6.8 -6.8
+#>         2 -6.9 -6.8
+#>         3 -7.0 -7.0
+#>         4 -6.9 -7.1
+#>         5 -6.7 -7.0
+#> 
+#> , , variable = theta
+#> 
+#>          chain
+#> iteration    1    2
+#>         1 0.28 0.21
+#>         2 0.19 0.20
+#>         3 0.16 0.17
+#>         4 0.20 0.36
+#>         5 0.25 0.34
+#> 
+#> # ... with 995 more iterations
+
+# Convert to data frame using posterior::as_draws_df
+as_draws_df(draws)
+#> # A draws_df: 1000 iterations, 2 chains, and 2 variables
+#>    lp__ theta
+#> 1  -6.8  0.28
+#> 2  -6.9  0.19
+#> 3  -7.0  0.16
+#> 4  -6.9  0.20
+#> 5  -6.7  0.25
+#> 6  -7.1  0.36
+#> 7  -9.0  0.55
+#> 8  -7.2  0.15
+#> 9  -6.8  0.23
+#> 10 -7.5  0.42
+#> # ... with 1990 more draws
+#> # ... hidden reserved variables {'.chain', '.iteration', '.draw'}
+
+# Plot posterior using bayesplot (ggplot2)
+mcmc_hist(fit_mcmc$draws("theta"))
+#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
+
+
+# For models fit using MCMC, if you like working with RStan's stanfit objects
+# then you can create one with rstan::read_stan_csv()
+# stanfit <- rstan::read_stan_csv(fit_mcmc$output_files())
+
+
+# Run 'optimize' method to get a point estimate (default is Stan's LBFGS algorithm)
+# and also demonstrate specifying data as a path to a file instead of a list
+my_data_file <- file.path(cmdstan_path(), "examples/bernoulli/bernoulli.data.json")
+fit_optim <- mod$optimize(data = my_data_file, seed = 123)
+#> Initial log joint probability = -9.51104 
+#>     Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes  
+#>        6      -5.00402   0.000103557   2.55661e-07           1           1        9    
+#> Optimization terminated normally:  
+#>   Convergence detected: relative gradient magnitude is below tolerance 
+#> Finished in  0.1 seconds.
+fit_optim$summary()
+#> # A tibble: 2 × 2
+#>   variable estimate
+#>   <chr>       <dbl>
+#> 1 lp__        -5.00
+#> 2 theta        0.2 
+
+# Run 'optimize' again with 'jacobian=TRUE' and then draw from laplace approximation
+# to the posterior
+fit_optim <- mod$optimize(data = my_data_file, jacobian = TRUE)
+#> Initial log joint probability = -8.53772 
+#>     Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes  
+#>        5      -6.74802   0.000313669   2.44314e-06           1           1        8    
+#> Optimization terminated normally:  
+#>   Convergence detected: relative gradient magnitude is below tolerance 
+#> Finished in  0.1 seconds.
+fit_laplace <- mod$laplace(data = my_data_file, mode = fit_optim, draws = 2000)
+#> Calculating Hessian 
+#> Calculating inverse of Cholesky factor 
+#> Generating draws 
+#> iteration: 0 
+#> iteration: 100 
+#> iteration: 200 
+#> iteration: 300 
+#> iteration: 400 
+#> iteration: 500 
+#> iteration: 600 
+#> iteration: 700 
+#> iteration: 800 
+#> iteration: 900 
+#> iteration: 1000 
+#> iteration: 1100 
+#> iteration: 1200 
+#> iteration: 1300 
+#> iteration: 1400 
+#> iteration: 1500 
+#> iteration: 1600 
+#> iteration: 1700 
+#> iteration: 1800 
+#> iteration: 1900 
+#> Finished in  0.1 seconds.
+fit_laplace$summary()
+#> # A tibble: 3 × 7
+#>   variable      mean median    sd   mad      q5      q95
+#>   <chr>        <dbl>  <dbl> <dbl> <dbl>   <dbl>    <dbl>
+#> 1 lp__        -7.24  -6.97  0.690 0.299 -8.66   -6.75   
+#> 2 lp_approx__ -0.502 -0.223 0.693 0.303 -1.94   -0.00213
+#> 3 theta        0.267  0.247 0.123 0.119  0.0995  0.501  
+
+# Run 'variational' method to use ADVI to approximate posterior
+fit_vb <- mod$variational(data = stan_data, seed = 123)
+#> ------------------------------------------------------------ 
+#> EXPERIMENTAL ALGORITHM: 
+#>   This procedure has not been thoroughly tested and may be unstable 
+#>   or buggy. The interface is subject to change. 
+#> ------------------------------------------------------------ 
+#> Gradient evaluation took 7e-06 seconds 
+#> 1000 transitions using 10 leapfrog steps per transition would take 0.07 seconds. 
+#> Adjust your expectations accordingly! 
+#> Begin eta adaptation. 
+#> Iteration:   1 / 250 [  0%]  (Adaptation) 
+#> Iteration:  50 / 250 [ 20%]  (Adaptation) 
+#> Iteration: 100 / 250 [ 40%]  (Adaptation) 
+#> Iteration: 150 / 250 [ 60%]  (Adaptation) 
+#> Iteration: 200 / 250 [ 80%]  (Adaptation) 
+#> Success! Found best value [eta = 1] earlier than expected. 
+#> Begin stochastic gradient ascent. 
+#>   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes  
+#>    100           -6.262             1.000            1.000 
+#>    200           -6.263             0.500            1.000 
+#>    300           -6.307             0.336            0.007   MEDIAN ELBO CONVERGED 
+#> Drawing a sample of size 1000 from the approximate posterior...  
+#> COMPLETED. 
+#> Finished in  0.1 seconds.
+fit_vb$summary()
+#> # A tibble: 3 × 7
+#>   variable      mean median    sd   mad     q5      q95
+#>   <chr>        <dbl>  <dbl> <dbl> <dbl>  <dbl>    <dbl>
+#> 1 lp__        -7.18  -6.94  0.588 0.259 -8.36  -6.75   
+#> 2 lp_approx__ -0.515 -0.221 0.692 0.303 -2.06  -0.00257
+#> 3 theta        0.263  0.246 0.115 0.113  0.106  0.481  
+mcmc_hist(fit_vb$draws("theta"))
+#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
+
+
+# Run 'pathfinder' method, a new alternative to the variational method
+fit_pf <- mod$pathfinder(data = stan_data, seed = 123)
+#> Path [1] :Initial log joint density = -11.008832 
+#> Path [1] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               5      -6.748e+00      9.383e-04   1.391e-05    1.000e+00  1.000e+00       126 -6.264e+00 -6.264e+00                   
+#> Path [1] :Best Iter: [3] ELBO (-6.195408) evaluations: (126) 
+#> Path [2] :Initial log joint density = -7.318450 
+#> Path [2] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               4      -6.748e+00      5.414e-03   1.618e-04    1.000e+00  1.000e+00       101 -6.251e+00 -6.251e+00                   
+#> Path [2] :Best Iter: [3] ELBO (-6.229174) evaluations: (101) 
+#> Path [3] :Initial log joint density = -12.374612 
+#> Path [3] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               5      -6.748e+00      1.419e-03   2.837e-05    1.000e+00  1.000e+00       126 -6.199e+00 -6.199e+00                   
+#> Path [3] :Best Iter: [5] ELBO (-6.199185) evaluations: (126) 
+#> Path [4] :Initial log joint density = -13.009824 
+#> Path [4] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+#>               5      -6.748e+00      1.677e-03   3.885e-05    1.000e+00  1.000e+00       126 -6.173e+00 -6.173e+00                   
+#> Path [4] :Best Iter: [5] ELBO (-6.172860) evaluations: (126) 
+#> Total log probability function evaluations:4379 
+#> Finished in  0.1 seconds.
+fit_pf$summary()
+#> # A tibble: 3 × 7
+#>   variable      mean median    sd   mad      q5    q95
+#>   <chr>        <dbl>  <dbl> <dbl> <dbl>   <dbl>  <dbl>
+#> 1 lp_approx__ -1.08  -0.728 0.886 0.304 -2.71   -0.511
+#> 2 lp__        -7.26  -6.96  0.738 0.297 -8.72   -6.75 
+#> 3 theta        0.249  0.230 0.120 0.121  0.0854  0.471
+mcmc_hist(fit_pf$draws("theta"))
+#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
+
+
+# Specifying initial values as a function
+fit_mcmc_w_init_fun <- mod$sample(
+  data = stan_data,
+  seed = 123,
+  chains = 2,
+  refresh = 0,
+  init = function() list(theta = runif(1))
+)
+#> Running MCMC with 2 sequential chains...
+#> 
+#> Chain 1 finished in 0.0 seconds.
+#> Chain 2 finished in 0.0 seconds.
+#> 
+#> Both chains finished successfully.
+#> Mean chain execution time: 0.0 seconds.
+#> Total execution time: 0.3 seconds.
+#> 
+fit_mcmc_w_init_fun_2 <- mod$sample(
+  data = stan_data,
+  seed = 123,
+  chains = 2,
+  refresh = 0,
+  init = function(chain_id) {
+    # silly but demonstrates optional use of chain_id
+    list(theta = 1 / (chain_id + 1))
+  }
+)
+#> Running MCMC with 2 sequential chains...
+#> 
+#> Chain 1 finished in 0.0 seconds.
+#> Chain 2 finished in 0.0 seconds.
+#> 
+#> Both chains finished successfully.
+#> Mean chain execution time: 0.0 seconds.
+#> Total execution time: 0.3 seconds.
+#> 
+fit_mcmc_w_init_fun_2$init()
+#> [[1]]
+#> [[1]]$theta
+#> [1] 0.5
+#> 
+#> 
+#> [[2]]
+#> [[2]]$theta
+#> [1] 0.3333333
+#> 
+#> 
+
+# Specifying initial values as a list of lists
+fit_mcmc_w_init_list <- mod$sample(
+  data = stan_data,
+  seed = 123,
+  chains = 2,
+  refresh = 0,
+  init = list(
+    list(theta = 0.75), # chain 1
+    list(theta = 0.25)  # chain 2
+  )
+)
+#> Running MCMC with 2 sequential chains...
+#> 
+#> Chain 1 finished in 0.0 seconds.
+#> Chain 2 finished in 0.0 seconds.
+#> 
+#> Both chains finished successfully.
+#> Mean chain execution time: 0.0 seconds.
+#> Total execution time: 0.3 seconds.
+#> 
+fit_optim_w_init_list <- mod$optimize(
+  data = stan_data,
+  seed = 123,
+  init = list(
+    list(theta = 0.75)
+  )
+)
+#> Initial log joint probability = -11.6657 
+#>     Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes  
+#>        6      -5.00402   0.000237915   9.55309e-07           1           1        9    
+#> Optimization terminated normally:  
+#>   Convergence detected: relative gradient magnitude is below tolerance 
+#> Finished in  0.1 seconds.
+fit_optim_w_init_list$init()
+#> [[1]]
+#> [[1]]$theta
+#> [1] 0.75
+#> 
+#> 
+# }
+
+
    +
    +
    + +
    + + +
    +

    Developed by Jonah Gabry, Rok Češnovar, Andrew Johnson.

    +
    + +
    +

    Site built with pkgdown 2.0.7.

    +
    + +
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b/docs/reference/model-method-sample.html @@ -24,7 +24,7 @@ cmdstanr - 0.6.0 + 0.7.0
    @@ -464,7 +464,9 @@

    See also

    model-method-expose_functions, model-method-format, model-method-generate-quantities, +model-method-laplace, model-method-optimize, +model-method-pathfinder, model-method-sample_mpi, model-method-variables, model-method-variational

    @@ -484,7 +486,7 @@

    Examples

    # Otherwise use the `path` argument to specify the location of your # CmdStan installation.) set_cmdstan_path(path = NULL) -#> CmdStan path set to: /Users/jgabry/.cmdstan/cmdstan-2.32.2 +#> CmdStan path set to: /Users/jgabry/.cmdstan/cmdstan-2.33.1 # Create a CmdStanModel object from a Stan program, # here using the example model that comes with CmdStan @@ -571,10 +573,22 @@

    Examples

    fit_mcmc$summary() #> # A tibble: 2 × 10 #> variable mean median sd mad q5 q95 rhat ess_bulk ess_tail -#> <chr> <num> <num> <num> <num> <num> <num> <num> <num> <num> +#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 lp__ -7.30 -7.03 0.721 0.380 -8.82 -6.75 1.00 902. 1006. #> 2 theta 0.247 0.233 0.122 0.129 0.0786 0.470 1.00 762. 712. +# Check sampling diagnostics +fit_mcmc$diagnostic_summary() +#> $num_divergent +#> [1] 0 0 +#> +#> $num_max_treedepth +#> [1] 0 0 +#> +#> $ebfmi +#> [1] 1.017555 1.250490 +#> + # Get posterior draws draws <- fit_mcmc$draws() print(draws) @@ -623,51 +637,8 @@

    Examples

    #> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`. -# Call CmdStan's diagnose and stansummary utilities -fit_mcmc$cmdstan_diagnose() -#> Processing csv files: /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpFBtN6X/bernoulli-202307251438-1-239737.csv, /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpFBtN6X/bernoulli-202307251438-2-239737.csv -#> -#> Checking sampler transitions treedepth. -#> Treedepth satisfactory for all transitions. -#> -#> Checking sampler transitions for divergences. -#> No divergent transitions found. -#> -#> Checking E-BFMI - sampler transitions HMC potential energy. -#> E-BFMI satisfactory. -#> -#> Effective sample size satisfactory. -#> -#> Split R-hat values satisfactory all parameters. -#> -#> Processing complete, no problems detected. -fit_mcmc$cmdstan_summary() -#> Inference for Stan model: bernoulli_model -#> 2 chains: each with iter=(1000,1000); warmup=(0,0); thin=(1,1); 2000 iterations saved. -#> -#> Warmup took (0.0040, 0.0040) seconds, 0.0080 seconds total -#> Sampling took (0.011, 0.011) seconds, 0.022 seconds total -#> -#> Mean MCSE StdDev 5% 50% 95% N_Eff N_Eff/s R_hat -#> -#> lp__ -7.3 2.6e-02 0.72 -8.8 -7.0 -6.8 781 35502 1.0 -#> accept_stat__ 0.92 8.3e-03 0.13 0.64 0.97 1.0 235 10662 1.0e+00 -#> stepsize__ 0.95 7.9e-02 0.079 0.87 1.0 1.0 1.0 46 2.0e+13 -#> treedepth__ 1.4 1.1e-02 0.48 1.0 1.0 2.0 1874 85179 1.0e+00 -#> n_leapfrog__ 2.5 1.4e-01 1.3 1.0 3.0 3.0 89 4050 1.0e+00 -#> divergent__ 0.00 nan 0.00 0.00 0.00 0.00 nan nan nan -#> energy__ 7.8 3.6e-02 1.00 6.8 7.5 9.6 775 35215 1.0e+00 -#> -#> theta 0.25 4.3e-03 0.12 0.079 0.23 0.47 796 36197 1.0 -#> -#> Samples were drawn using hmc with nuts. -#> For each parameter, N_Eff is a crude measure of effective sample size, -#> and R_hat is the potential scale reduction factor on split chains (at -#> convergence, R_hat=1). - # For models fit using MCMC, if you like working with RStan's stanfit objects # then you can create one with rstan::read_stan_csv() - # stanfit <- rstan::read_stan_csv(fit_mcmc$output_files()) @@ -681,24 +652,64 @@

    Examples

    #> Optimization terminated normally: #> Convergence detected: relative gradient magnitude is below tolerance #> Finished in 0.1 seconds. - fit_optim$summary() #> # A tibble: 2 × 2 #> variable estimate -#> <chr> <num> +#> <chr> <dbl> #> 1 lp__ -5.00 #> 2 theta 0.2 +# Run 'optimize' again with 'jacobian=TRUE' and then draw from laplace approximation +# to the posterior +fit_optim <- mod$optimize(data = my_data_file, jacobian = TRUE) +#> Initial log joint probability = -7.01351 +#> Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes +#> 4 -6.74802 0.000544739 0.000133582 0.9055 0.9055 7 +#> Optimization terminated normally: +#> Convergence detected: relative gradient magnitude is below tolerance +#> Finished in 0.1 seconds. +fit_laplace <- mod$laplace(data = my_data_file, mode = fit_optim, draws = 2000) +#> Calculating Hessian +#> Calculating inverse of Cholesky factor +#> Generating draws +#> iteration: 0 +#> iteration: 100 +#> iteration: 200 +#> iteration: 300 +#> iteration: 400 +#> iteration: 500 +#> iteration: 600 +#> iteration: 700 +#> iteration: 800 +#> iteration: 900 +#> iteration: 1000 +#> iteration: 1100 +#> iteration: 1200 +#> iteration: 1300 +#> iteration: 1400 +#> iteration: 1500 +#> iteration: 1600 +#> iteration: 1700 +#> iteration: 1800 +#> iteration: 1900 +#> Finished in 0.1 seconds. +fit_laplace$summary() +#> # A tibble: 3 × 7 +#> variable mean median sd mad q5 q95 +#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 lp__ -7.24 -6.98 0.692 0.314 -8.61 -6.75 +#> 2 lp_approx__ -0.500 -0.233 0.688 0.317 -1.89 -0.00172 +#> 3 theta 0.269 0.248 0.123 0.122 0.0989 0.503 -# Run 'variational' method to approximate the posterior (default is meanfield ADVI) +# Run 'variational' method to use ADVI to approximate posterior fit_vb <- mod$variational(data = stan_data, seed = 123) #> ------------------------------------------------------------ #> EXPERIMENTAL ALGORITHM: #> This procedure has not been thoroughly tested and may be unstable #> or buggy. The interface is subject to change. #> ------------------------------------------------------------ -#> Gradient evaluation took 9e-06 seconds -#> 1000 transitions using 10 leapfrog steps per transition would take 0.09 seconds. +#> Gradient evaluation took 7e-06 seconds +#> 1000 transitions using 10 leapfrog steps per transition would take 0.07 seconds. #> Adjust your expectations accordingly! #> Begin eta adaptation. #> Iteration: 1 / 250 [ 0%] (Adaptation) @@ -715,20 +726,47 @@

    Examples

    #> Drawing a sample of size 1000 from the approximate posterior... #> COMPLETED. #> Finished in 0.1 seconds. - fit_vb$summary() #> # A tibble: 3 × 7 #> variable mean median sd mad q5 q95 -#> <chr> <num> <num> <num> <num> <num> <num> +#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 lp__ -7.18 -6.94 0.588 0.259 -8.36 -6.75 #> 2 lp_approx__ -0.515 -0.221 0.692 0.303 -2.06 -0.00257 #> 3 theta 0.263 0.246 0.115 0.113 0.106 0.481 - -# Plot approximate posterior using bayesplot mcmc_hist(fit_vb$draws("theta")) #> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`. +# Run 'pathfinder' method, a new alternative to the variational method +fit_pf <- mod$pathfinder(data = stan_data, seed = 123) +#> Path [1] :Initial log joint density = -11.008832 +#> Path [1] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes +#> 5 -6.748e+00 9.383e-04 1.391e-05 1.000e+00 1.000e+00 126 -6.264e+00 -6.264e+00 +#> Path [1] :Best Iter: [3] ELBO (-6.195408) evaluations: (126) +#> Path [2] :Initial log joint density = -7.318450 +#> Path [2] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes +#> 4 -6.748e+00 5.414e-03 1.618e-04 1.000e+00 1.000e+00 101 -6.251e+00 -6.251e+00 +#> Path [2] :Best Iter: [3] ELBO (-6.229174) evaluations: (101) +#> Path [3] :Initial log joint density = -12.374612 +#> Path [3] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes +#> 5 -6.748e+00 1.419e-03 2.837e-05 1.000e+00 1.000e+00 126 -6.199e+00 -6.199e+00 +#> Path [3] :Best Iter: [5] ELBO (-6.199185) evaluations: (126) +#> Path [4] :Initial log joint density = -13.009824 +#> Path [4] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes +#> 5 -6.748e+00 1.677e-03 3.885e-05 1.000e+00 1.000e+00 126 -6.173e+00 -6.173e+00 +#> Path [4] :Best Iter: [5] ELBO (-6.172860) evaluations: (126) +#> Total log probability function evaluations:4379 +#> Finished in 0.1 seconds. +fit_pf$summary() +#> # A tibble: 3 × 7 +#> variable mean median sd mad q5 q95 +#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 lp_approx__ -1.08 -0.728 0.886 0.304 -2.71 -0.511 +#> 2 lp__ -7.26 -6.96 0.738 0.297 -8.72 -6.75 +#> 3 theta 0.249 0.230 0.120 0.121 0.0854 0.471 +mcmc_hist(fit_pf$draws("theta")) +#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`. + # Specifying initial values as a function fit_mcmc_w_init_fun <- mod$sample( diff --git a/docs/reference/model-method-sample_mpi.html b/docs/reference/model-method-sample_mpi.html index 53d2eb1a..5dd06c42 100644 --- a/docs/reference/model-method-sample_mpi.html +++ b/docs/reference/model-method-sample_mpi.html @@ -43,7 +43,7 @@ cmdstanr - 0.6.0 + 0.7.0 @@ -477,7 +477,9 @@

    See also

    model-method-expose_functions, model-method-format, model-method-generate-quantities, +model-method-laplace, model-method-optimize, +model-method-pathfinder, model-method-sample, model-method-variables, model-method-variational

    diff --git a/docs/reference/model-method-variables.html b/docs/reference/model-method-variables.html index f26828ae..6df7de09 100644 --- a/docs/reference/model-method-variables.html +++ b/docs/reference/model-method-variables.html @@ -24,7 +24,7 @@ cmdstanr - 0.6.0 + 0.7.0 @@ -141,7 +141,9 @@

    See also

    model-method-expose_functions, model-method-format, model-method-generate-quantities, +model-method-laplace, model-method-optimize, +model-method-pathfinder, model-method-sample_mpi, model-method-sample, model-method-variational

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+++ b/docs/reference/model-method-variational.html @@ -1,10 +1,15 @@ Run Stan's variational approximation algorithms — model-method-variational • cmdstanr @@ -22,7 +27,7 @@ cmdstanr - 0.6.0 + 0.7.0 @@ -110,11 +115,16 @@

    Run Stan's variational approximation algorithms

    The $variational() method of a CmdStanModel object runs -Stan's variational Bayes (ADVI) algorithms.

    -

    Any argument left as NULL will default to the default value used by the -installed version of CmdStan. See the +Stan's Automatic Differentiation Variational Inference (ADVI) algorithms. +The approximation is a Gaussian in the unconstrained variable space. Stan +implements two ADVI algorithms: the algorithm="meanfield" option uses a +fully factorized Gaussian for the approximation; the algorithm="fullrank" +option uses a Gaussian with a full-rank covariance matrix for the +approximation. See the CmdStan User’s Guide for more details.

    +

    Any argument left as NULL will default to the default value used by the +installed version of CmdStan.

    @@ -138,7 +148,8 @@

    Run Stan's variational approximation algorithms

    adapt_iter = NULL, tol_rel_obj = NULL, eval_elbo = NULL, - output_samples = NULL + output_samples = NULL, + draws = NULL )
    @@ -299,6 +310,11 @@

    Arguments

    output_samples
    +

    (positive integer) Use draws argument instead. +output_samples will be deprecated in the future.

    + + +
    draws

    (positive integer) Number of approximate posterior samples to draw and save.

    @@ -308,16 +324,6 @@

    Value

    A CmdStanVB object.

    - -
    -

    Details

    -

    CmdStan can fit a variational approximation to the posterior. The -approximation is a Gaussian in the unconstrained variable space. Stan -implements two variational algorithms. The algorithm="meanfield" option -uses a fully factorized Gaussian for the approximation. The -algorithm="fullrank" option uses a Gaussian with a full-rank covariance -matrix for the approximation.

    -

    -- CmdStan Interface User's Guide

    @@ -353,7 +361,7 @@

    Examples

    # Otherwise use the `path` argument to specify the location of your # CmdStan installation.) set_cmdstan_path(path = NULL) -#> CmdStan path set to: /Users/jgabry/.cmdstan/cmdstan-2.32.2 +#> CmdStan path set to: /Users/jgabry/.cmdstan/cmdstan-2.33.1 # Create a CmdStanModel object from a Stan program, # here using the example model that comes with CmdStan @@ -440,10 +448,22 @@

    Examples

    fit_mcmc$summary() #> # A tibble: 2 × 10 #> variable mean median sd mad q5 q95 rhat ess_bulk ess_tail -#> <chr> <num> <num> <num> <num> <num> <num> <num> <num> <num> +#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 lp__ -7.30 -7.03 0.721 0.380 -8.82 -6.75 1.00 902. 1006. #> 2 theta 0.247 0.233 0.122 0.129 0.0786 0.470 1.00 762. 712. +# Check sampling diagnostics +fit_mcmc$diagnostic_summary() +#> $num_divergent +#> [1] 0 0 +#> +#> $num_max_treedepth +#> [1] 0 0 +#> +#> $ebfmi +#> [1] 1.017555 1.250490 +#> + # Get posterior draws draws <- fit_mcmc$draws() print(draws) @@ -492,51 +512,8 @@

    Examples

    #> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`. -# Call CmdStan's diagnose and stansummary utilities -fit_mcmc$cmdstan_diagnose() -#> Processing csv files: /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpFBtN6X/bernoulli-202307251438-1-4ea737.csv, /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpFBtN6X/bernoulli-202307251438-2-4ea737.csv -#> -#> Checking sampler transitions treedepth. -#> Treedepth satisfactory for all transitions. -#> -#> Checking sampler transitions for divergences. -#> No divergent transitions found. -#> -#> Checking E-BFMI - sampler transitions HMC potential energy. -#> E-BFMI satisfactory. -#> -#> Effective sample size satisfactory. -#> -#> Split R-hat values satisfactory all parameters. -#> -#> Processing complete, no problems detected. -fit_mcmc$cmdstan_summary() -#> Inference for Stan model: bernoulli_model -#> 2 chains: each with iter=(1000,1000); warmup=(0,0); thin=(1,1); 2000 iterations saved. -#> -#> Warmup took (0.0040, 0.0040) seconds, 0.0080 seconds total -#> Sampling took (0.011, 0.011) seconds, 0.022 seconds total -#> -#> Mean MCSE StdDev 5% 50% 95% N_Eff N_Eff/s R_hat -#> -#> lp__ -7.3 2.6e-02 0.72 -8.8 -7.0 -6.8 781 35502 1.0 -#> accept_stat__ 0.92 8.3e-03 0.13 0.64 0.97 1.0 235 10662 1.0e+00 -#> stepsize__ 0.95 7.9e-02 0.079 0.87 1.0 1.0 1.0 46 2.0e+13 -#> treedepth__ 1.4 1.1e-02 0.48 1.0 1.0 2.0 1874 85179 1.0e+00 -#> n_leapfrog__ 2.5 1.4e-01 1.3 1.0 3.0 3.0 89 4050 1.0e+00 -#> divergent__ 0.00 nan 0.00 0.00 0.00 0.00 nan nan nan -#> energy__ 7.8 3.6e-02 1.00 6.8 7.5 9.6 775 35215 1.0e+00 -#> -#> theta 0.25 4.3e-03 0.12 0.079 0.23 0.47 796 36197 1.0 -#> -#> Samples were drawn using hmc with nuts. -#> For each parameter, N_Eff is a crude measure of effective sample size, -#> and R_hat is the potential scale reduction factor on split chains (at -#> convergence, R_hat=1). - # For models fit using MCMC, if you like working with RStan's stanfit objects # then you can create one with rstan::read_stan_csv() - # stanfit <- rstan::read_stan_csv(fit_mcmc$output_files()) @@ -550,24 +527,64 @@

    Examples

    #> Optimization terminated normally: #> Convergence detected: relative gradient magnitude is below tolerance #> Finished in 0.1 seconds. - fit_optim$summary() #> # A tibble: 2 × 2 #> variable estimate -#> <chr> <num> +#> <chr> <dbl> #> 1 lp__ -5.00 #> 2 theta 0.2 +# Run 'optimize' again with 'jacobian=TRUE' and then draw from laplace approximation +# to the posterior +fit_optim <- mod$optimize(data = my_data_file, jacobian = TRUE) +#> Initial log joint probability = -7.23249 +#> Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes +#> 4 -6.74802 0.00456563 0.000121975 1 1 7 +#> Optimization terminated normally: +#> Convergence detected: relative gradient magnitude is below tolerance +#> Finished in 0.1 seconds. +fit_laplace <- mod$laplace(data = my_data_file, mode = fit_optim, draws = 2000) +#> Calculating Hessian +#> Calculating inverse of Cholesky factor +#> Generating draws +#> iteration: 0 +#> iteration: 100 +#> iteration: 200 +#> iteration: 300 +#> iteration: 400 +#> iteration: 500 +#> iteration: 600 +#> iteration: 700 +#> iteration: 800 +#> iteration: 900 +#> iteration: 1000 +#> iteration: 1100 +#> iteration: 1200 +#> iteration: 1300 +#> iteration: 1400 +#> iteration: 1500 +#> iteration: 1600 +#> iteration: 1700 +#> iteration: 1800 +#> iteration: 1900 +#> Finished in 0.1 seconds. +fit_laplace$summary() +#> # A tibble: 3 × 7 +#> variable mean median sd mad q5 q95 +#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 lp__ -7.25 -6.99 0.712 0.326 -8.66 -6.75 +#> 2 lp_approx__ -0.506 -0.244 0.715 0.329 -1.94 -0.00187 +#> 3 theta 0.269 0.250 0.124 0.126 0.101 0.500 -# Run 'variational' method to approximate the posterior (default is meanfield ADVI) +# Run 'variational' method to use ADVI to approximate posterior fit_vb <- mod$variational(data = stan_data, seed = 123) #> ------------------------------------------------------------ #> EXPERIMENTAL ALGORITHM: #> This procedure has not been thoroughly tested and may be unstable #> or buggy. The interface is subject to change. #> ------------------------------------------------------------ -#> Gradient evaluation took 9e-06 seconds -#> 1000 transitions using 10 leapfrog steps per transition would take 0.09 seconds. +#> Gradient evaluation took 7e-06 seconds +#> 1000 transitions using 10 leapfrog steps per transition would take 0.07 seconds. #> Adjust your expectations accordingly! #> Begin eta adaptation. #> Iteration: 1 / 250 [ 0%] (Adaptation) @@ -584,20 +601,47 @@

    Examples

    #> Drawing a sample of size 1000 from the approximate posterior... #> COMPLETED. #> Finished in 0.1 seconds. - fit_vb$summary() #> # A tibble: 3 × 7 #> variable mean median sd mad q5 q95 -#> <chr> <num> <num> <num> <num> <num> <num> +#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 lp__ -7.18 -6.94 0.588 0.259 -8.36 -6.75 #> 2 lp_approx__ -0.515 -0.221 0.692 0.303 -2.06 -0.00257 #> 3 theta 0.263 0.246 0.115 0.113 0.106 0.481 - -# Plot approximate posterior using bayesplot mcmc_hist(fit_vb$draws("theta")) #> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`. +# Run 'pathfinder' method, a new alternative to the variational method +fit_pf <- mod$pathfinder(data = stan_data, seed = 123) +#> Path [1] :Initial log joint density = -11.008832 +#> Path [1] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes +#> 5 -6.748e+00 9.383e-04 1.391e-05 1.000e+00 1.000e+00 126 -6.264e+00 -6.264e+00 +#> Path [1] :Best Iter: [3] ELBO (-6.195408) evaluations: (126) +#> Path [2] :Initial log joint density = -7.318450 +#> Path [2] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes +#> 4 -6.748e+00 5.414e-03 1.618e-04 1.000e+00 1.000e+00 101 -6.251e+00 -6.251e+00 +#> Path [2] :Best Iter: [3] ELBO (-6.229174) evaluations: (101) +#> Path [3] :Initial log joint density = -12.374612 +#> Path [3] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes +#> 5 -6.748e+00 1.419e-03 2.837e-05 1.000e+00 1.000e+00 126 -6.199e+00 -6.199e+00 +#> Path [3] :Best Iter: [5] ELBO (-6.199185) evaluations: (126) +#> Path [4] :Initial log joint density = -13.009824 +#> Path [4] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes +#> 5 -6.748e+00 1.677e-03 3.885e-05 1.000e+00 1.000e+00 126 -6.173e+00 -6.173e+00 +#> Path [4] :Best Iter: [5] ELBO (-6.172860) evaluations: (126) +#> Total log probability function evaluations:4379 +#> Finished in 0.1 seconds. +fit_pf$summary() +#> # A tibble: 3 × 7 +#> variable mean median sd mad q5 q95 +#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 lp_approx__ -1.08 -0.728 0.886 0.304 -2.71 -0.511 +#> 2 lp__ -7.26 -6.96 0.738 0.297 -8.72 -6.75 +#> 3 theta 0.249 0.230 0.120 0.121 0.0854 0.471 +mcmc_hist(fit_pf$draws("theta")) +#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`. + # Specifying initial values as a function fit_mcmc_w_init_fun <- mod$sample( diff --git a/docs/reference/read_cmdstan_csv.html b/docs/reference/read_cmdstan_csv.html index 2106577e..d5b0c50d 100644 --- a/docs/reference/read_cmdstan_csv.html +++ b/docs/reference/read_cmdstan_csv.html @@ -22,7 +22,7 @@ cmdstanr - 0.6.0 + 0.7.0 @@ -173,7 +173,7 @@

    Arguments

    Value

    -

    as_cmdstan_fit() returns a CmdStanMCMC, CmdStanMLE, or +

    as_cmdstan_fit() returns a CmdStanMCMC, CmdStanMLE, CmdStanLaplace or CmdStanVB object. Some methods typically defined for those objects will not work (e.g. save_data_file()) but the important methods like $summary(), $draws(), $sampler_diagnostics() and others will work fine.

    @@ -206,7 +206,8 @@

    Value

    specified) of post-warmup draws of the sampler diagnostic variables.

    For optimization the returned list also includes the following components:

    • point_estimates: Point estimates for the model parameters.

      • -

    For variational inference the returned list also +

    For laplace and +variational inference the returned list also includes the following components:

    • draws: A draws_matrix (or different format if format is specified) of draws from the approximate posterior distribution.

      • @@ -222,10 +223,10 @@

      Examples

      fit1 <- cmdstanr_example("logistic", method = "sample", save_warmup = TRUE) csv_files <- fit1$output_files() print(csv_files) -#> [1] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpFBtN6X/logistic-202307251438-1-0afc76.csv" -#> [2] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpFBtN6X/logistic-202307251438-2-0afc76.csv" -#> [3] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpFBtN6X/logistic-202307251438-3-0afc76.csv" -#> [4] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpFBtN6X/logistic-202307251438-4-0afc76.csv" +#> [1] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-202311081035-1-32a06f.csv" +#> [2] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-202311081035-2-32a06f.csv" +#> [3] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-202311081035-3-32a06f.csv" +#> [4] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-202311081035-4-32a06f.csv" # Creating fitting model objects @@ -233,9 +234,9 @@

      Examples

      fit2 <- as_cmdstan_fit(csv_files) fit2$print("beta") #> variable mean median sd mad q5 q95 rhat ess_bulk ess_tail -#> beta[1] -0.67 -0.66 0.25 0.25 -1.09 -0.26 1.00 4467 2955 -#> beta[2] -0.27 -0.27 0.22 0.22 -0.63 0.09 1.00 4244 3209 -#> beta[3] 0.68 0.68 0.26 0.26 0.25 1.12 1.00 4340 3315 +#> beta[1] -0.67 -0.66 0.25 0.24 -1.08 -0.28 1.00 4402 2852 +#> beta[2] -0.27 -0.27 0.22 0.22 -0.65 0.09 1.00 4396 2793 +#> beta[3] 0.68 0.67 0.27 0.26 0.27 1.12 1.00 4545 3039 # Using read_cmdstan_csv # @@ -245,9 +246,9 @@

      Examples

      #> List of 8 #> $ metadata :List of 40 #> ..$ stan_version_major : num 2 -#> ..$ stan_version_minor : num 32 -#> ..$ stan_version_patch : num 2 -#> ..$ start_datetime : chr "2023-07-25 20:38:31 UTC" +#> ..$ stan_version_minor : num 33 +#> ..$ stan_version_patch : num 0 +#> ..$ start_datetime : chr "2023-11-08 17:35:02 UTC" #> ..$ method : chr "sample" #> ..$ save_warmup : num 1 #> ..$ thin : num 1 @@ -264,14 +265,14 @@

      Examples

      #> ..$ num_chains : num 1 #> ..$ id : num [1:4] 1 2 3 4 #> ..$ init : num [1:4] 2 2 2 2 -#> ..$ seed : num 1.15e+09 +#> ..$ seed : num 1.63e+09 #> ..$ refresh : num 100 #> ..$ sig_figs : num -1 -#> ..$ profile_file : chr "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpFBtN6X/logistic-profile-202307251438-1-5569d9.csv" -#> ..$ stanc_version : chr "stanc3 v2.32.2" +#> ..$ profile_file : chr "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/logistic-profile-202311081035-1-02259b.csv" +#> ..$ stanc_version : chr "stanc3 v2.33.1" #> ..$ sampler_diagnostics : chr [1:6] "accept_stat__" "stepsize__" "treedepth__" "n_leapfrog__" ... #> ..$ variables : chr [1:105] "lp__" "alpha" "beta[1]" "beta[2]" ... -#> ..$ step_size_adaptation: num [1:4] 0.666 0.702 0.658 0.727 +#> ..$ step_size_adaptation: num [1:4] 0.731 0.842 0.658 0.727 #> ..$ model_name : chr "logistic_model" #> ..$ adapt_engaged : num 1 #> ..$ adapt_delta : num 0.8 @@ -282,9 +283,9 @@

      Examples

      #> ..$ threads_per_chain : num 1 #> ..$ time :'data.frame': 4 obs. of 4 variables: #> .. ..$ chain_id: num [1:4] 1 2 3 4 -#> .. ..$ warmup : num [1:4] 0.07 0.081 0.077 0.071 -#> .. ..$ sampling: num [1:4] 0.068 0.075 0.065 0.062 -#> .. ..$ total : num [1:4] 0.138 0.156 0.142 0.133 +#> .. ..$ warmup : num [1:4] 0.071 0.073 0.078 0.075 +#> .. ..$ sampling: num [1:4] 0.075 0.089 0.078 0.073 +#> .. ..$ total : num [1:4] 0.146 0.162 0.156 0.148 #> ..$ stan_variable_sizes :List of 4 #> .. ..$ lp__ : num 1 #> .. ..$ alpha : num 1 @@ -296,35 +297,35 @@

      Examples

      #> ..$ total : int NA #> ..$ chains:'data.frame': 4 obs. of 4 variables: #> .. ..$ chain_id: num [1:4] 1 2 3 4 -#> .. ..$ warmup : num [1:4] 0.07 0.081 0.077 0.071 -#> .. ..$ sampling: num [1:4] 0.068 0.075 0.065 0.062 -#> .. ..$ total : num [1:4] 0.138 0.156 0.142 0.133 +#> .. ..$ warmup : num [1:4] 0.071 0.073 0.078 0.075 +#> .. ..$ sampling: num [1:4] 0.075 0.089 0.078 0.073 +#> .. ..$ total : num [1:4] 0.146 0.162 0.156 0.148 #> $ inv_metric :List of 4 -#> ..$ 1: num [1:4] 0.0426 0.0631 0.0525 0.0739 -#> ..$ 2: num [1:4] 0.0483 0.0658 0.0426 0.065 -#> ..$ 3: num [1:4] 0.0473 0.0591 0.0532 0.0814 -#> ..$ 4: num [1:4] 0.0485 0.0583 0.05 0.0707 +#> ..$ 1: num [1:4] 0.0464 0.0532 0.0478 0.0741 +#> ..$ 2: num [1:4] 0.0374 0.0624 0.0482 0.0688 +#> ..$ 3: num [1:4] 0.042 0.0545 0.0473 0.0754 +#> ..$ 4: num [1:4] 0.0411 0.0563 0.0539 0.0609 #> $ step_size :List of 4 -#> ..$ 1: num 0.666 -#> ..$ 2: num 0.702 +#> ..$ 1: num 0.731 +#> ..$ 2: num 0.842 #> ..$ 3: num 0.658 #> ..$ 4: num 0.727 -#> $ warmup_draws : 'draws_array' num [1:1000, 1:4, 1:105] -105.5 -105.5 -105.5 -70.7 -66.2 ... +#> $ warmup_draws : 'draws_array' num [1:1000, 1:4, 1:105] -78.1 -78.1 -78.1 -64.4 -66.7 ... #> ..- attr(*, "dimnames")=List of 3 #> .. ..$ iteration: chr [1:1000] "1" "2" "3" "4" ... #> .. ..$ chain : chr [1:4] "1" "2" "3" "4" #> .. ..$ variable : chr [1:105] "lp__" "alpha" "beta[1]" "beta[2]" ... -#> $ post_warmup_draws : 'draws_array' num [1:1000, 1:4, 1:105] -65.2 -64.4 -65.4 -64.4 -70.3 ... +#> $ post_warmup_draws : 'draws_array' num [1:1000, 1:4, 1:105] -68.5 -66.2 -68 -68.3 -67.2 ... #> ..- attr(*, "dimnames")=List of 3 #> .. ..$ iteration: chr [1:1000] "1" "2" "3" "4" ... #> .. ..$ chain : chr [1:4] "1" "2" "3" "4" #> .. ..$ variable : chr [1:105] "lp__" "alpha" "beta[1]" "beta[2]" ... -#> $ warmup_sampler_diagnostics : 'draws_array' num [1:1000, 1:4, 1:6] 6.68e-01 0.00 1.27e-204 9.99e-01 9.89e-01 ... +#> $ warmup_sampler_diagnostics : 'draws_array' num [1:1000, 1:4, 1:6] 1 0 0 1 0.515 ... #> ..- attr(*, "dimnames")=List of 3 #> .. ..$ iteration: chr [1:1000] "1" "2" "3" "4" ... #> .. ..$ chain : chr [1:4] "1" "2" "3" "4" #> .. ..$ variable : chr [1:6] "accept_stat__" "stepsize__" "treedepth__" "n_leapfrog__" ... -#> $ post_warmup_sampler_diagnostics: 'draws_array' num [1:1000, 1:4, 1:6] 0.997 0.935 0.852 0.98 0.644 ... +#> $ post_warmup_sampler_diagnostics: 'draws_array' num [1:1000, 1:4, 1:6] 0.856 1 0.856 0.984 1 ... #> ..- attr(*, "dimnames")=List of 3 #> .. ..$ iteration: chr [1:1000] "1" "2" "3" "4" ... #> .. ..$ chain : chr [1:4] "1" "2" "3" "4" diff --git a/docs/reference/read_sample_csv.html b/docs/reference/read_sample_csv.html index af34b60a..3e9b12d5 100644 --- a/docs/reference/read_sample_csv.html +++ b/docs/reference/read_sample_csv.html @@ -17,7 +17,7 @@ cmdstanr - 0.6.0 + 0.7.0 diff --git a/docs/reference/register_knitr_engine.html b/docs/reference/register_knitr_engine.html index 1e451770..5a66dd01 100644 --- a/docs/reference/register_knitr_engine.html +++ b/docs/reference/register_knitr_engine.html @@ -20,7 +20,7 @@ cmdstanr - 0.6.0 + 0.7.0 diff --git a/docs/reference/set_cmdstan_path.html b/docs/reference/set_cmdstan_path.html index a91b01e8..d467d271 100644 --- a/docs/reference/set_cmdstan_path.html +++ b/docs/reference/set_cmdstan_path.html @@ -21,7 +21,7 @@ cmdstanr - 0.6.0 + 0.7.0 diff --git a/docs/reference/stan_threads.html b/docs/reference/stan_threads.html index eeeb82fa..bf8c1ef3 100644 --- a/docs/reference/stan_threads.html +++ b/docs/reference/stan_threads.html @@ -17,7 +17,7 @@ cmdstanr - 0.6.0 + 0.7.0 diff --git a/docs/reference/write_stan_file.html b/docs/reference/write_stan_file.html index 9b772e61..10ffeb6e 100644 --- a/docs/reference/write_stan_file.html +++ b/docs/reference/write_stan_file.html @@ -24,7 +24,7 @@ cmdstanr - 0.6.0 + 0.7.0 @@ -176,7 +176,7 @@

      Examples

      stan_program <- " data { int<lower=0> N; - int<lower=0,upper=1> y[N]; + array[N] int<lower=0,upper=1> y; } parameters { real<lower=0,upper=1> theta; @@ -188,21 +188,21 @@

      Examples

      f <- write_stan_file(stan_program) print(f) -#> [1] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpFBtN6X/model_08f9e456ca04f3d3244db00f16ea5748.stan" +#> [1] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpGCiVFY/model_7f12fc190dd23b0e462f7d73040dd97e.stan" lines <- readLines(f) print(lines) -#> [1] "" "data {" -#> [3] " int<lower=0> N;" " int<lower=0,upper=1> y[N];" -#> [5] "}" "parameters {" -#> [7] " real<lower=0,upper=1> theta;" "}" -#> [9] "model {" " y ~ bernoulli(theta);" -#> [11] "}" "" +#> [1] "" "data {" +#> [3] " int<lower=0> N;" " array[N] int<lower=0,upper=1> y;" +#> [5] "}" "parameters {" +#> [7] " real<lower=0,upper=1> theta;" "}" +#> [9] "model {" " y ~ bernoulli(theta);" +#> [11] "}" "" cat(lines, sep = "\n") #> #> data { #> int<lower=0> N; -#> int<lower=0,upper=1> y[N]; +#> array[N] int<lower=0,upper=1> y; #> } #> parameters { #> real<lower=0,upper=1> theta; diff --git a/docs/reference/write_stan_json.html b/docs/reference/write_stan_json.html index a241a6d9..d09ca22c 100644 --- a/docs/reference/write_stan_json.html +++ b/docs/reference/write_stan_json.html @@ -17,7 +17,7 @@ cmdstanr - 0.6.0 + 0.7.0 @@ -167,13 +167,13 @@

      Examples

      #> "N": 5, #> "K": 2, #> "x": [ -#> [1.39067866394794, 0.962441316104017], -#> [-0.473566508639385, -0.892862153643971], -#> [-2.10865772647623, 0.757598190570521], -#> [-0.366087824635297, -0.120402278774405], -#> [1.44942517823716, -0.444483690720062] +#> [0.539316345307751, 1.20165111819522], +#> [0.027826781785157, -0.219847554976374], +#> [-0.171509078741447, 1.08053600860179], +#> [1.01158266854045, 0.0611935712707024], +#> [-0.426238537104135, 0.270940591850318] #> ], -#> "y": [5, 11, 1, 9, 11], +#> "y": [10, 8, 13, 10, 12], #> "z": [1, 0] #> } diff --git a/docs/reference/write_stan_tempfile.html b/docs/reference/write_stan_tempfile.html index 4e9c4ac1..10725142 100644 --- a/docs/reference/write_stan_tempfile.html +++ b/docs/reference/write_stan_tempfile.html @@ -17,7 +17,7 @@ cmdstanr - 0.6.0 + 0.7.0 diff --git a/docs/sitemap.xml b/docs/sitemap.xml index a9c13dbb..6866e056 100644 --- a/docs/sitemap.xml +++ b/docs/sitemap.xml @@ -1,23 +1,95 @@ + + https://mc-stan.org/cmdstanr/404.html + + + https://mc-stan.org/cmdstanr/LICENSE-text.html + + + https://mc-stan.org/cmdstanr/LICENSE.html + + + https://mc-stan.org/cmdstanr/articles/articles-online-only/opencl.html + + + https://mc-stan.org/cmdstanr/articles/children/settings-knitr.html + + + https://mc-stan.org/cmdstanr/articles/cmdstanr-internals.html + + + https://mc-stan.org/cmdstanr/articles/cmdstanr.html + + + https://mc-stan.org/cmdstanr/articles/deprecations.html + + + https://mc-stan.org/cmdstanr/articles/index.html + + + https://mc-stan.org/cmdstanr/articles/opencl.html + + + https://mc-stan.org/cmdstanr/articles/posterior.html + + + https://mc-stan.org/cmdstanr/articles/profiling.html + + + https://mc-stan.org/cmdstanr/articles/r-markdown.html + + + https://mc-stan.org/cmdstanr/articles/r_markdown.html + + + https://mc-stan.org/cmdstanr/authors.html + https://mc-stan.org/cmdstanr/index.html + + https://mc-stan.org/cmdstanr/news/index.html + + + https://mc-stan.org/cmdstanr/pull_request_template.html + https://mc-stan.org/cmdstanr/reference/CmdStanDiagnose.html + + https://mc-stan.org/cmdstanr/reference/CmdStanFit-method-save_output_files.html + https://mc-stan.org/cmdstanr/reference/CmdStanGQ.html + + https://mc-stan.org/cmdstanr/reference/CmdStanLaplace.html + https://mc-stan.org/cmdstanr/reference/CmdStanMCMC.html https://mc-stan.org/cmdstanr/reference/CmdStanMLE.html + + https://mc-stan.org/cmdstanr/reference/CmdStanModel-method-compile.html + + + https://mc-stan.org/cmdstanr/reference/CmdStanModel-method-optimize.html + + + https://mc-stan.org/cmdstanr/reference/CmdStanModel-method-sample.html + + + https://mc-stan.org/cmdstanr/reference/CmdStanModel-method-variational.html + https://mc-stan.org/cmdstanr/reference/CmdStanModel.html + + https://mc-stan.org/cmdstanr/reference/CmdStanPathfinder.html + https://mc-stan.org/cmdstanr/reference/CmdStanVB.html @@ -36,6 +108,9 @@ https://mc-stan.org/cmdstanr/reference/cmdstan_model.html + + https://mc-stan.org/cmdstanr/reference/cmdstan_path.html + https://mc-stan.org/cmdstanr/reference/cmdstanr-package.html @@ -54,21 +129,39 @@ https://mc-stan.org/cmdstanr/reference/fit-method-code.html + + https://mc-stan.org/cmdstanr/reference/fit-method-constrain_variables.html + https://mc-stan.org/cmdstanr/reference/fit-method-diagnostic_summary.html https://mc-stan.org/cmdstanr/reference/fit-method-draws.html + + https://mc-stan.org/cmdstanr/reference/fit-method-grad_log_prob.html + https://mc-stan.org/cmdstanr/reference/fit-method-gradients.html + + https://mc-stan.org/cmdstanr/reference/fit-method-hessian.html + https://mc-stan.org/cmdstanr/reference/fit-method-init.html + + https://mc-stan.org/cmdstanr/reference/fit-method-init_model_methods.html + https://mc-stan.org/cmdstanr/reference/fit-method-inv_metric.html + + https://mc-stan.org/cmdstanr/reference/fit-method-inverse_metric.html + + + https://mc-stan.org/cmdstanr/reference/fit-method-log_prob.html + https://mc-stan.org/cmdstanr/reference/fit-method-loo.html @@ -108,6 +201,18 @@ https://mc-stan.org/cmdstanr/reference/fit-method-time.html + + https://mc-stan.org/cmdstanr/reference/fit-method-unconstrain_draws.html + + + https://mc-stan.org/cmdstanr/reference/fit-method-unconstrain_variables.html + + + https://mc-stan.org/cmdstanr/reference/fit-method-variable_skeleton.html + + + https://mc-stan.org/cmdstanr/reference/index.html + https://mc-stan.org/cmdstanr/reference/install_cmdstan.html @@ -120,15 +225,24 @@ https://mc-stan.org/cmdstanr/reference/model-method-diagnose.html + + https://mc-stan.org/cmdstanr/reference/model-method-expose_functions.html + https://mc-stan.org/cmdstanr/reference/model-method-format.html https://mc-stan.org/cmdstanr/reference/model-method-generate-quantities.html + + https://mc-stan.org/cmdstanr/reference/model-method-laplace.html + https://mc-stan.org/cmdstanr/reference/model-method-optimize.html + + https://mc-stan.org/cmdstanr/reference/model-method-pathfinder.html + https://mc-stan.org/cmdstanr/reference/model-method-sample.html @@ -165,22 +279,4 @@ https://mc-stan.org/cmdstanr/reference/write_stan_tempfile.html - - https://mc-stan.org/cmdstanr/articles/articles-online-only/opencl.html - - - https://mc-stan.org/cmdstanr/articles/children/settings-knitr.html - - - https://mc-stan.org/cmdstanr/articles/cmdstanr-internals.html - - - https://mc-stan.org/cmdstanr/articles/cmdstanr.html - - - https://mc-stan.org/cmdstanr/articles/profiling.html - - - https://mc-stan.org/cmdstanr/articles/r-markdown.html - From 15590bfdafe45ec6f2f5cb1b9d968ff85e6b2c9f Mon Sep 17 00:00:00 2001 From: jgabry Date: Wed, 8 Nov 2023 12:28:56 -0700 Subject: [PATCH 05/19] add pathfinder to main vignette closes #867 --- vignettes/cmdstanr.Rmd | 49 ++++++++++++++++++++++++++++++++---------- 1 file changed, 38 insertions(+), 11 deletions(-) diff --git a/vignettes/cmdstanr.Rmd b/vignettes/cmdstanr.Rmd index 83419cc1..b81b7c41 100644 --- a/vignettes/cmdstanr.Rmd +++ b/vignettes/cmdstanr.Rmd @@ -341,8 +341,8 @@ stanfit <- rstan::read_stan_csv(fit$output_files()) CmdStanR also supports running Stan's optimization algorithms and its algorithms for variational approximation of full Bayesian inference. These are run via the -`$optimize()`, `$laplace()`, and `$variational()` methods, which are called in a -similar way to the `$sample()` method demonstrated above. +`$optimize()`, `$laplace()`, `$variational()`, and `pathfinder()` methods, which +are called in a similar way to the `$sample()` method demonstrated above. ### Optimization @@ -405,11 +405,13 @@ fit_laplace$print("theta") mcmc_hist(fit_laplace$draws("theta"), binwidth = 0.025) ``` -### Variational Bayes +### Variational (ADVI) + +We can run Stan's experimental Automatic Differentiation Variational Inference +(ADVI) using the [`$variational()`](https://mc-stan.org/cmdstanr/reference/model-method-variational.html) +method. For details on the ADVI algorithm see the +[CmdStan User's Guide](https://mc-stan.org/docs/cmdstan-guide/variational-inference-algorithm-advi.html). -We can run Stan's experimental variational Bayes algorithm (ADVI) using the -[`$variational()`](https://mc-stan.org/cmdstanr/reference/model-method-variational.html) -method. ```{r variational} fit_vb <- mod$variational( @@ -418,12 +420,36 @@ fit_vb <- mod$variational( draws = 4000 ) fit_vb$print("theta") +mcmc_hist(fit_vb$draws("theta"), binwidth = 0.025) +``` + +### Variational (Pathfinder) + +Stan version 2.33 introduced a new variational method called Pathfinder, +which is intended to be faster and more stable than ADVI. For details on how +Pathfinder works see the section in the +[CmdStan User's Guide](https://mc-stan.org/docs/cmdstan-guide/pathfinder-intro.html#pathfinder-intro). + +```{r pathfinder} +fit_pf <- mod$pathfinder( + data = data_list, + seed = 123, + draws = 4000 +) +fit_pf$print("theta") ``` -Let's extract the draws, make the same plot we made after MCMC and Laplace -approximation, and compare them all. In this simple example the distributions -are quite similar, but this will not always be the case. +Let's extract the draws, make the same plot we made after running the other +algorithms, and compare them all. approximation, and compare them all. In this +simple example the distributions are quite similar, but this will not always be +the case for more challenging problems. + +```{r plot-compare-pf, message = FALSE} +mcmc_hist(fit_pf$draws("theta"), binwidth = 0.025) + + ggplot2::labs(subtitle = "Approximate posterior from pathfinder") + + ggplot2::xlim(0, 1) +``` ```{r plot-compare-vb, message = FALSE} mcmc_hist(fit_vb$draws("theta"), binwidth = 0.025) + ggplot2::labs(subtitle = "Approximate posterior from variational") + @@ -440,12 +466,13 @@ mcmc_hist(fit$draws("theta"), binwidth = 0.025) + ggplot2::xlim(0, 1) ``` -For more details on the `$optimize()`, `$laplace()` and `$variational()` -methods, follow these links to their documentation pages. +For more details on the `$optimize()`, `$laplace()`, `$variational()`, and +`pathfinder()` methods, follow these links to their documentation pages. * [`$optimize()`](https://mc-stan.org/cmdstanr/reference/model-method-optimize.html) * [`$laplace()`](https://mc-stan.org/cmdstanr/reference/model-method-laplace.html) * [`$variational()`](https://mc-stan.org/cmdstanr/reference/model-method-variational.html) +* [`$pathfinder()`](https://mc-stan.org/cmdstanr/reference/model-method-pathfinder.html) ## Saving fitted model objects From 923308a832781eaa5a5d9060217a6cad13fcaf09 Mon Sep 17 00:00:00 2001 From: jgabry Date: Wed, 8 Nov 2023 12:29:38 -0700 Subject: [PATCH 06/19] add @andrjohns as coauthor of main vignette --- vignettes/cmdstanr.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/vignettes/cmdstanr.Rmd b/vignettes/cmdstanr.Rmd index b81b7c41..cac4f47d 100644 --- a/vignettes/cmdstanr.Rmd +++ b/vignettes/cmdstanr.Rmd @@ -1,6 +1,6 @@ --- title: "Getting started with CmdStanR" -author: "Jonah Gabry and Rok Češnovar" +author: "Jonah Gabry, Rok Češnovar, and Andrew Johnson" output: rmarkdown::html_vignette: toc: true From 87e4f776ce77aa1a18177f2342855e1e5a759188 Mon Sep 17 00:00:00 2001 From: jgabry Date: Wed, 8 Nov 2023 12:55:17 -0700 Subject: [PATCH 07/19] more vignette edits --- docs/articles/cmdstanr.html | 157 ++++++++++++------ .../figure-html/plot-compare-pf-1.png | Bin 0 -> 48003 bytes .../figure-html/variational-1.png | Bin 0 -> 37488 bytes docs/reference/Rplot001.png | Bin 1011 -> 5039 bytes docs/reference/cmdstanr-package.html | 24 +-- man/cmdstanr-package.Rd | 4 +- vignettes/children/comparison-with-rstan.md | 6 +- vignettes/cmdstanr.Rmd | 6 +- 8 files changed, 129 insertions(+), 68 deletions(-) create mode 100644 docs/articles/cmdstanr_files/figure-html/plot-compare-pf-1.png create mode 100644 docs/articles/cmdstanr_files/figure-html/variational-1.png diff --git a/docs/articles/cmdstanr.html b/docs/articles/cmdstanr.html index a1119cfb..808859cd 100644 --- a/docs/articles/cmdstanr.html +++ b/docs/articles/cmdstanr.html @@ -131,8 +131,8 @@

      Getting started with CmdStanR

      -

      Jonah Gabry and -Rok Češnovar

      +

      Jonah Gabry, +Rok Češnovar, and Andrew Johnson

      Source: vignettes/cmdstanr.Rmd @@ -496,34 +496,33 @@

      Sampler diagnostic warnings a suffers from divergences.

       fit_with_warning <- cmdstanr_example("schools")
      -
      Chain 3 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
      -
      Chain 3 Exception: normal_lpdf: Scale parameter is 0, but must be positive! (in '/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/Rtmpo8OtTr/model-138066584791a.stan', line 14, column 2 to column 41)
      -
      Chain 3 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
      -
      Chain 3 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
      -
      Chain 3
      -
      Warning: 123 of 4000 (3.0%) transitions ended with a divergence.
+
      Warning: 124 of 4000 (3.0%) transitions ended with a divergence.
+See https://mc-stan.org/misc/warnings for details.
      
+
      Warning: 1 of 4 chains had an E-BFMI less than 0.2.
 See https://mc-stan.org/misc/warnings for details.
      
 
      After fitting there is a warning about divergences. We can also
 regenerate this warning message later using
 fit$diagnostic_summary().
      
-
      +
       diagnostics <- fit_with_warning$diagnostic_summary()
      
-
      Warning: 123 of 4000 (3.0%) transitions ended with a divergence.
+
      Warning: 124 of 4000 (3.0%) transitions ended with a divergence.
+See https://mc-stan.org/misc/warnings for details.
      
+
      Warning: 1 of 4 chains had an E-BFMI less than 0.2.
 See https://mc-stan.org/misc/warnings for details.
      
-
      +
       print(diagnostics)
      
 
      $num_divergent
-[1] 24 21 36 42
+[1]  3 12 97 12
 
 $num_max_treedepth
 [1] 0 0 0 0
 
 $ebfmi
-[1] 0.25 0.44 0.22 0.35
      
-
      +[1] 0.20 0.29 0.29 0.43
      
+
       # number of divergences reported in warning is the sum of the per chain values
 sum(diagnostics$num_divergent)
      
-
      [1] 123
      
+
      [1] 124
      
 
      
 
      
 
      CmdStan’s diagnose utility
@@ -544,7 +543,7 @@ 
      Create a stanfit object
      +
 
      
 
      
@@ -554,14 +553,15 @@ 
      Running optimization and
 
      CmdStanR also supports running Stan’s optimization algorithms and its
 algorithms for variational approximation of full Bayesian inference.
 These are run via the $optimize(), $laplace(),
-and $variational() methods, which are called in a similar
-way to the $sample() method demonstrated above.
      
+$variational(), and $pathfinder() methods,
+which are called in a similar way to the $sample() method
+demonstrated above.
      
 
      
 
      Optimization
 
      
 
      We can find the (penalized) maximum likelihood estimate (MLE) using
 $optimize().
      
-
      +
       fit_mle <- mod$optimize(data = data_list, seed = 123)
      
 
      Initial log joint probability = -9.51104 
     Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes  
@@ -569,18 +569,18 @@ 
      Optimization
      +
       fit_mle$print() # includes lp__ (log prob calculated by Stan program)
      
 
       variable estimate
     lp__     -5.00
     theta     0.20
      
-
      +
       fit_mle$mle("theta")
      
 
      theta 
   0.2 
      
 
      Here’s a plot comparing the penalized MLE to the posterior
 distribution of theta.
      
-
      +
       mcmc_hist(fit$draws("theta")) +
   vline_at(fit_mle$mle("theta"), size = 1.5)
      
 
      
@@ -591,7 +591,7 @@ 
      OptimizationMaximum
 Likelihood Estimation section of the CmdStan User’s Guide for more
 details.
      
-
      +
       fit_map <- mod$optimize(
   data = data_list,
   jacobian = TRUE,
@@ -608,19 +608,19 @@ 
      OptimizationLaplace Approximation
 
      
 
      The $laplace()
-produces a sample from a normal approximation centered at the mode of a
-distribution in the unconstrained space. If the mode is a MAP estimate,
-the samples provide an estimate of the mean and standard deviation of
-the posterior distribution. If the mode is the MLE, the sample provides
-an estimate of the standard error of the likelihood. Whether the mode is
-the MAP or MLE depends on the value of the jacobian
-argument when running optimization. See the Laplace
+method produces a sample from a normal approximation centered at the
+mode of a distribution in the unconstrained space. If the mode is a MAP
+estimate, the samples provide an estimate of the mean and standard
+deviation of the posterior distribution. If the mode is the MLE, the
+sample provides an estimate of the standard error of the likelihood.
+Whether the mode is the MAP or MLE depends on the value of the
+jacobian argument when running optimization. See the Laplace
 Sampling chapter of the CmdStan User’s Guide for more details.
      
 
      Here we pass in the fit_map object from above as the
 mode argument. If mode is omitted then
 optimization will be run internally before taking draws from the normal
 approximation.
      
-
      +
       fit_laplace <- mod$laplace(
     mode = fit_map,
     draws = 4000,
@@ -636,21 +636,22 @@ 
      Laplace Approximation
      
-
      +
       fit_laplace$print("theta")
      
 
       variable mean median   sd  mad   q5  q95
     theta 0.27   0.25 0.12 0.12 0.10 0.50
      
-
      +
       mcmc_hist(fit_laplace$draws("theta"), binwidth = 0.025)
      
 
      
 
      
 
      
-
      Variational Bayes
+
      Variational (ADVI)
 
      
-
      We can run Stan’s experimental variational Bayes algorithm (ADVI)
-using the $variational()
-method.
      
-
      +
      We can run Stan’s experimental Automatic Differentiation Variational
+Inference (ADVI) using the $variational()
+method. For details on the ADVI algorithm see the CmdStan
+User’s Guide.
      
+
+
      
+
      Variational (Pathfinder)
+
      
+
      Stan version 2.33 introduced a new variational method called
+Pathfinder, which is intended to be faster and more stable than ADVI.
+For details on how Pathfinder works see the section in the CmdStan
+User’s Guide. Pathfinder is run using the $pathfinder()
+method.
      
+
      +fit_pf <- mod$pathfinder(
+  data = data_list,
+  seed = 123,
+  draws = 4000
+)
      
+
      Path [1] :Initial log joint density = -11.008832 
+Path [1] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+              5      -6.748e+00      9.383e-04   1.391e-05    1.000e+00  1.000e+00       126 -6.264e+00 -6.264e+00                   
+Path [1] :Best Iter: [3] ELBO (-6.195408) evaluations: (126) 
+Path [2] :Initial log joint density = -7.318450 
+Path [2] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+              4      -6.748e+00      5.414e-03   1.618e-04    1.000e+00  1.000e+00       101 -6.251e+00 -6.251e+00                   
+Path [2] :Best Iter: [3] ELBO (-6.229174) evaluations: (101) 
+Path [3] :Initial log joint density = -12.374612 
+Path [3] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+              5      -6.748e+00      1.419e-03   2.837e-05    1.000e+00  1.000e+00       126 -6.199e+00 -6.199e+00                   
+Path [3] :Best Iter: [5] ELBO (-6.199185) evaluations: (126) 
+Path [4] :Initial log joint density = -13.009824 
+Path [4] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
+              5      -6.748e+00      1.677e-03   3.885e-05    1.000e+00  1.000e+00       126 -6.173e+00 -6.173e+00                   
+Path [4] :Best Iter: [5] ELBO (-6.172860) evaluations: (126) 
+Total log probability function evaluations:4379 
+Finished in  0.1 seconds.
      
 
      +fit_pf$print("theta")
      
+
       variable mean median   sd  mad   q5  q95
+    theta 0.25   0.24 0.12 0.12 0.08 0.47
      
+
      Let’s extract the draws, make the same plot we made after running the
+other algorithms, and compare them all. approximation, and compare them
+all. In this simple example the distributions are quite similar, but
+this will not always be the case for more challenging problems.
      
+
      +mcmc_hist(fit_pf$draws("theta"), binwidth = 0.025) +
+  ggplot2::labs(subtitle = "Approximate posterior from pathfinder") +
+  ggplot2::xlim(0, 1)
      
+
      
+
       mcmc_hist(fit_vb$draws("theta"), binwidth = 0.025) +
   ggplot2::labs(subtitle = "Approximate posterior from variational") +
   ggplot2::xlim(0, 1)
      
 
      
-
      +
       mcmc_hist(fit_laplace$draws("theta"), binwidth = 0.025) +
   ggplot2::labs(subtitle = "Approximate posterior from Laplace") +
   ggplot2::xlim(0, 1)
      
 
      
-
      +
       mcmc_hist(fit$draws("theta"), binwidth = 0.025) +
   ggplot2::labs(subtitle = "Posterior from MCMC") +
   ggplot2::xlim(0, 1)
      
 
      
 
      For more details on the $optimize(),
-$laplace() and $variational() methods, follow
-these links to their documentation pages.
      
+$laplace(), $variational(), and
+pathfinder() methods, follow these links to their
+documentation pages.
      
 
 
      
 
      
@@ -719,7 +767,7 @@ 
      Saving fitted model objects
      +
       fit$save_object(file = "fit.RDS")
 
 # can be read back in using readRDS
@@ -731,7 +779,7 @@ 
      Saving fitted model objects$save_object()
 and replace saveRDS with the much faster
 qsave() function from the qs package.
      
-
      +
       # Load CmdStan output files into the fitted model object.
 fit$draws() # Load posterior draws into the object.
 try(fit$sampler_diagnostics(), silent = TRUE) # Load sampler diagnostics.
@@ -747,7 +795,7 @@ 
      Saving fitted model objects
      +
       # Load posterior draws into the fitted model object and omit other output.
 fit$draws()
 
@@ -780,9 +828,12 @@ 
      Advantages of RStanDeveloping
+using CmdStanR.)
      
 
    • Avoids use of R6 classes, which may result in more familiar
 syntax for many R users.
      • 
+
    • CRAN binaries available for Mac and Windows.
      •

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a/docs/reference/cmdstanr-package.html b/docs/reference/cmdstanr-package.html index 694d6a38..6e29914b 100644 --- a/docs/reference/cmdstanr-package.html +++ b/docs/reference/cmdstanr-package.html @@ -134,9 +134,11 @@

    Different ways of interfac

    Advantages of RStan

    • Allows other developers to distribute R packages with pre-compiled -Stan programs (like rstanarm) on CRAN.

      • +Stan programs (like rstanarm) on CRAN. (Note: As of 2023, this can +mostly be achieved with CmdStanR as well. See Developing using CmdStanR.)

    • Avoids use of R6 classes, which may result in more familiar syntax for many R users.

      • +

    • CRAN binaries available for Mac and Windows.

    @@ -289,7 +291,7 @@

    Examples

    #> [1] 0 0 #> #> $ebfmi -#> [1] 1.017555 1.250490 +#> [1] 1.0 1.3 #> # Get posterior draws @@ -365,9 +367,9 @@

    Examples

    # Run 'optimize' again with 'jacobian=TRUE' and then draw from laplace approximation # to the posterior fit_optim <- mod$optimize(data = my_data_file, jacobian = TRUE) -#> Initial log joint probability = -16.0056 +#> Initial log joint probability = -7.20447 #> Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes -#> 5 -6.74802 0.00186556 5.60208e-05 1 1 8 +#> 4 -6.74802 0.00428356 0.000109727 1 1 7 #> Optimization terminated normally: #> Convergence detected: relative gradient magnitude is below tolerance #> Finished in 0.1 seconds. @@ -395,14 +397,14 @@

    Examples

    #> iteration: 1700 #> iteration: 1800 #> iteration: 1900 -#> Finished in 0.1 seconds. +#> Finished in 0.2 seconds. fit_laplace$summary() #> # A tibble: 3 × 7 #> variable mean median sd mad q5 q95 #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 lp__ -7.24 -6.98 0.709 0.312 -8.60 -6.75 -#> 2 lp_approx__ -0.506 -0.238 0.711 0.320 -1.94 -0.00243 -#> 3 theta 0.265 0.245 0.123 0.121 0.0981 0.496 +#> 1 lp__ -7.23 -6.96 0.694 0.287 -8.53 -6.75 +#> 2 lp_approx__ -0.493 -0.211 0.714 0.286 -1.87 -0.00222 +#> 3 theta 0.264 0.248 0.120 0.120 0.0954 0.491 # Run 'variational' method to use ADVI to approximate posterior fit_vb <- mod$variational(data = stan_data, seed = 123) @@ -486,7 +488,7 @@

    Examples

    #> #> Both chains finished successfully. #> Mean chain execution time: 0.0 seconds. -#> Total execution time: 0.3 seconds. +#> Total execution time: 0.7 seconds. #> fit_mcmc_w_init_fun_2 <- mod$sample( data = stan_data, @@ -515,7 +517,7 @@

    Examples

    #> #> [[2]] #> [[2]]$theta -#> [1] 0.3333333 +#> [1] 0.33 #> #> @@ -537,7 +539,7 @@

    Examples

    #> #> Both chains finished successfully. #> Mean chain execution time: 0.0 seconds. -#> Total execution time: 0.3 seconds. +#> Total execution time: 0.5 seconds. #> fit_optim_w_init_list <- mod$optimize( data = stan_data, diff --git a/man/cmdstanr-package.Rd b/man/cmdstanr-package.Rd index aa695619..cabe3d8d 100644 --- a/man/cmdstanr-package.Rd +++ b/man/cmdstanr-package.Rd @@ -34,9 +34,11 @@ algorithms, and writing results to output files. \subsection{Advantages of RStan}{ \itemize{ \item Allows other developers to distribute R packages with \emph{pre-compiled} -Stan programs (like \strong{rstanarm}) on CRAN. +Stan programs (like \strong{rstanarm}) on CRAN. (Note: As of 2023, this can +mostly be achieved with CmdStanR as well. See \href{https://mc-stan.org/cmdstanr/articles/cmdstanr-internals.html#developing-using-cmdstanr}{Developing using CmdStanR}.) \item Avoids use of R6 classes, which may result in more familiar syntax for many R users. +\item CRAN binaries available for Mac and Windows. } } diff --git a/vignettes/children/comparison-with-rstan.md b/vignettes/children/comparison-with-rstan.md index ed1628f1..8f8e61f7 100644 --- a/vignettes/children/comparison-with-rstan.md +++ b/vignettes/children/comparison-with-rstan.md @@ -10,10 +10,14 @@ results to output files. ### Advantages of RStan * Allows other developers to distribute R packages with -_pre-compiled_ Stan programs (like **rstanarm**) on CRAN. +_pre-compiled_ Stan programs (like **rstanarm**) on CRAN. (Note: As of 2023, +this can mostly be achieved with CmdStanR as well. See +[Developing using CmdStanR](https://mc-stan.org/cmdstanr/articles/cmdstanr-internals.html#developing-using-cmdstanr).) * Avoids use of R6 classes, which may result in more familiar syntax for many R users. +* CRAN binaries available for Mac and Windows. + ### Advantages of CmdStanR * Compatible with latest versions of Stan. Keeping up with Stan releases is diff --git a/vignettes/cmdstanr.Rmd b/vignettes/cmdstanr.Rmd index cac4f47d..b9b2e341 100644 --- a/vignettes/cmdstanr.Rmd +++ b/vignettes/cmdstanr.Rmd @@ -341,7 +341,7 @@ stanfit <- rstan::read_stan_csv(fit$output_files()) CmdStanR also supports running Stan's optimization algorithms and its algorithms for variational approximation of full Bayesian inference. These are run via the -`$optimize()`, `$laplace()`, `$variational()`, and `pathfinder()` methods, which +`$optimize()`, `$laplace()`, `$variational()`, and `$pathfinder()` methods, which are called in a similar way to the `$sample()` method demonstrated above. ### Optimization @@ -380,7 +380,7 @@ fit_map <- mod$optimize( ### Laplace Approximation The [`$laplace()`](https://mc-stan.org/cmdstanr/reference/model-method-laplace.html) -produces a sample from a normal approximation centered at the mode of a +method produces a sample from a normal approximation centered at the mode of a distribution in the unconstrained space. If the mode is a MAP estimate, the samples provide an estimate of the mean and standard deviation of the posterior distribution. If the mode is the MLE, the sample provides an estimate of the @@ -429,6 +429,8 @@ Stan version 2.33 introduced a new variational method called Pathfinder, which is intended to be faster and more stable than ADVI. For details on how Pathfinder works see the section in the [CmdStan User's Guide](https://mc-stan.org/docs/cmdstan-guide/pathfinder-intro.html#pathfinder-intro). +Pathfinder is run using the [`$pathfinder()`](https://mc-stan.org/cmdstanr/reference/model-method-pathfinder.html) +method. ```{r pathfinder} fit_pf <- mod$pathfinder( From 3103deb5f7f9ea5aeef42e767353fb3bb39638ce Mon Sep 17 00:00:00 2001 From: jgabry Date: Wed, 8 Nov 2023 13:07:12 -0700 Subject: [PATCH 08/19] temporary fix for pkgdown rendering issue in posterior.Rmd vignette --- docs/articles/posterior.html | 214 +++++++++++++++++------------------ vignettes/posterior.Rmd | 13 ++- 2 files changed, 118 insertions(+), 109 deletions(-) diff --git a/docs/articles/posterior.html b/docs/articles/posterior.html index 1d5cc3e4..783351c1 100644 --- a/docs/articles/posterior.html +++ b/docs/articles/posterior.html @@ -147,24 +147,34 @@

    Summary statistics
     fit <- cmdstanr::cmdstanr_example("schools", method = "sample")

    -
    Warning: 129 of 4000 (3.0%) transitions ended with a divergence.
+
    Warning: 191 of 4000 (5.0%) transitions ended with a divergence.
 See https://mc-stan.org/misc/warnings for details.
    
 
     fit$summary()
    
-
    
[38;5;246m# A tibble: 11 × 10
[39m
-   variable   mean median    sd   mad      q5   q95  rhat ess_bulk ess_tail
-   
[3m
[38;5;246m<chr>
[39m
[23m     
[3m
[38;5;246m<dbl>
[39m
[23m  
[3m
[38;5;246m<dbl>
[39m
[23m 
[3m
[38;5;246m<dbl>
[39m
[23m 
[3m
[38;5;246m<dbl>
[39m
[23m   
[3m
[38;5;246m<dbl>
[39m
[23m 
[3m
[38;5;246m<dbl>
[39m
[23m 
[3m
[38;5;246m<dbl>
[39m
[23m    
[3m
[38;5;246m<dbl>
[39m
[23m    
[3m
[38;5;246m<dbl>
[39m
[23m
-
[38;5;250m 1
[39m lp__     -
[31m57
[39m
[31m.
[39m
[31m8
[39m  -
[31m58
[39m
[31m.
[39m
[31m2
[39m   5.49  5.70 -
[31m66
[39m
[31m.
[39m
[31m4
[39m   -
[31m48
[39m
[31m.
[39m
[31m3
[39m  1.02     167.     73.4
-
[38;5;250m 2
[39m mu         6.41   6.52  4.26  3.98  -
[31m0
[39m
[31m.
[39m
[31m818
[39m  13.3  1.00     879.   
[4m1
[24m035. 
-
[38;5;250m 3
[39m tau        5.25   4.42  3.65  3.35   1.05   12.3  1.02     165.     74.2
-
[38;5;250m 4
[39m theta[1]   9.31   8.49  7.20  5.99  -
[31m0
[39m
[31m.
[39m
[31m848
[39m  22.7  1.00    
[4m1
[24m019.   
[4m1
[24m427. 
-
[38;5;250m 5
[39m theta[2]   6.79   6.59  5.67  5.19  -
[31m2
[39m
[31m.
[39m
[31m48
[39m   16.0  1.00    
[4m1
[24m144.   
[4m1
[24m560. 
-
[38;5;250m 6
[39m theta[3]   5.26   5.83  6.76  5.56  -
[31m6
[39m
[31m.
[39m
[31m39
[39m   15.5  1.00    
[4m1
[24m331.   
[4m1
[24m585. 
-
[38;5;250m 7
[39m theta[4]   6.50   6.53  5.87  5.41  -
[31m3
[39m
[31m.
[39m
[31m19
[39m   16.4  1.00    
[4m1
[24m180.   
[4m1
[24m550. 
-
[38;5;250m 8
[39m theta[5]   4.65   5.06  5.63  5.08  -
[31m5
[39m
[31m.
[39m
[31m11
[39m   13.6  1.01    
[4m1
[24m173.   
[4m1
[24m709. 
-
[38;5;250m 9
[39m theta[6]   5.45   5.88  5.82  5.38  -
[31m4
[39m
[31m.
[39m
[31m57
[39m   14.4  1.00    
[4m1
[24m435.   
[4m1
[24m879. 
-
[38;5;250m10
[39m theta[7]   9.10   8.57  6.15  5.73  -
[31m0
[39m
[31m.
[39m
[31m198
[39m  19.7  1.00     857.    987. 
-
[38;5;250m11
[39m theta[8]   7.07   6.89  6.47  5.57  -
[31m3
[39m
[31m.
[39m
[31m26
[39m   17.8  1.01    
[4m1
[24m358.   
[4m1
[24m753. 
    
+
       variable       mean     median       sd      mad          q5       q95
+1      lp__ -57.917364 -58.126700 5.227350 5.348035 -66.0514450 -47.99920
+2        mu   6.421661   6.560445 3.994261 3.728717  -0.1207198  13.16044
+3       tau   5.211890   4.447485 3.457118 3.180740   1.0590100  12.09968
+4  theta[1]   9.159825   8.066620 6.731183 5.516199   0.1858543  21.43202
+5  theta[2]   6.750832   6.740980 5.320603 4.835678  -1.7502315  15.66325
+6  theta[3]   5.564623   5.963075 6.485297 5.310451  -5.2589090  15.65180
+7  theta[4]   6.506969   6.552735 5.753473 4.933870  -2.8831600  15.96813
+8  theta[5]   4.602639   4.969540 5.596007 4.792030  -5.1313190  13.08000
+9  theta[6]   5.451744   5.604770 5.781909 4.976021  -4.4380795  14.88214
+10 theta[7]   9.105948   8.421645 6.077003 5.363817   0.4464001  20.13298
+11 theta[8]   6.904986   6.666570 6.591653 5.244594  -3.4154830  18.00026
+       rhat   ess_bulk   ess_tail
+1  1.037320   87.01265   18.13542
+2  1.019548  741.86144 1129.22230
+3  1.041517   74.40670   16.64168
+4  1.012043  615.84853 1757.06290
+5  1.008696 1279.50483 2218.60618
+6  1.002604 1345.71326 1786.80529
+7  1.010044 1291.76655 1900.28317
+8  1.008882 1209.92612 1848.18547
+9  1.034876 1464.53969 1882.25434
+10 1.008780  554.66658 1685.21958
+11 1.034006 1102.79917 1784.94313
    
 
    By default all variables are summaries with the follow functions:
    
 
@@ -172,19 +182,18 @@ 
    Summary statistics
     fit$summary(variables = c("mu", "tau"))
    
-
    
[38;5;246m# A tibble: 2 × 10
[39m
-  variable  mean median    sd   mad     q5   q95  rhat ess_bulk ess_tail
-  
[3m
[38;5;246m<chr>
[39m
[23m    
[3m
[38;5;246m<dbl>
[39m
[23m  
[3m
[38;5;246m<dbl>
[39m
[23m 
[3m
[38;5;246m<dbl>
[39m
[23m 
[3m
[38;5;246m<dbl>
[39m
[23m  
[3m
[38;5;246m<dbl>
[39m
[23m 
[3m
[38;5;246m<dbl>
[39m
[23m 
[3m
[38;5;246m<dbl>
[39m
[23m    
[3m
[38;5;246m<dbl>
[39m
[23m    
[3m
[38;5;246m<dbl>
[39m
[23m
-
[38;5;250m1
[39m mu        6.41   6.52  4.26  3.98 -
[31m0
[39m
[31m.
[39m
[31m818
[39m  13.3  1.00     879.   
[4m1
[24m035. 
-
[38;5;250m2
[39m tau       5.25   4.42  3.65  3.35  1.05   12.3  1.02     165.     74.2
    
+
      variable     mean   median       sd      mad         q5      q95     rhat
+1       mu 6.421661 6.560445 3.994261 3.728717 -0.1207198 13.16044 1.019548
+2      tau 5.211890 4.447485 3.457118 3.180740  1.0590100 12.09968 1.041517
+  ess_bulk   ess_tail
+1 741.8614 1129.22230
+2  74.4067   16.64168
    
 
    We can additionally change which functions are used
    
 
     fit$summary(variables = c("mu", "tau"), mean, sd)
    
-
    
[38;5;246m# A tibble: 2 × 3
[39m
-  variable  mean    sd
-  
[3m
[38;5;246m<chr>
[39m
[23m    
[3m
[38;5;246m<dbl>
[39m
[23m 
[3m
[38;5;246m<dbl>
[39m
[23m
-
[38;5;250m1
[39m mu        6.41  4.26
-
[38;5;250m2
[39m tau       5.25  3.65
    
+
      variable     mean       sd
+1       mu 6.421661 3.994261
+2      tau 5.211890 3.457118
    
 
    To summarize all variables with non-default functions, it is
 necessary to set explicitly set the variables argument, either to
 NULL or the full vector of variable names.
    
@@ -194,24 +203,23 @@ 
    Summary statistics
     fit$summary(variables = NULL, "mean", "median")
    -
    
[38;5;246m# A tibble: 11 × 3
[39m
-   variable   mean median
-   
[3m
[38;5;246m<chr>
[39m
[23m     
[3m
[38;5;246m<dbl>
[39m
[23m  
[3m
[38;5;246m<dbl>
[39m
[23m
-
[38;5;250m 1
[39m lp__     -
[31m57
[39m
[31m.
[39m
[31m8
[39m  -
[31m58
[39m
[31m.
[39m
[31m2
[39m 
-
[38;5;250m 2
[39m mu         6.41   6.52
-
[38;5;250m 3
[39m tau        5.25   4.42
-
[38;5;250m 4
[39m theta[1]   9.31   8.49
-
[38;5;250m 5
[39m theta[2]   6.79   6.59
-
[38;5;250m 6
[39m theta[3]   5.26   5.83
-
[38;5;250m 7
[39m theta[4]   6.50   6.53
-
[38;5;250m 8
[39m theta[5]   4.65   5.06
-
[38;5;250m 9
[39m theta[6]   5.45   5.88
-
[38;5;250m10
[39m theta[7]   9.10   8.57
-
[38;5;250m11
[39m theta[8]   7.07   6.89
    +
       variable       mean     median
+1      lp__ -57.917364 -58.126700
+2        mu   6.421661   6.560445
+3       tau   5.211890   4.447485
+4  theta[1]   9.159825   8.066620
+5  theta[2]   6.750832   6.740980
+6  theta[3]   5.564623   5.963075
+7  theta[4]   6.506969   6.552735
+8  theta[5]   4.602639   4.969540
+9  theta[6]   5.451744   5.604770
+10 theta[7]   9.105948   8.421645
+11 theta[8]   6.904986   6.666570

    Summary functions can be specified by character string, function, or -using a formula (or anything else supported by [rlang::as_function]). If -these arguments are named, those names will be used in the tibble -output. If the summary results are named they will take precedence.

    +using a formula (or anything else supported by +    rlang::as_function()). If these arguments are named, those +names will be used in the tibble output. If the summary results are +named they will take precedence.

     my_sd <- function(x) c(My_SD = sd(x))
 fit$summary(
@@ -222,38 +230,32 @@ 
    Summary statistics~quantile(.x, probs = c(0.1, 0.9)),
   Minimum = function(x) min(x)
 )
    -
    
[38;5;246m# A tibble: 2 × 7
[39m
-  variable  MEAN median My_SD `10%` `90%` Minimum
-  
[3m
[38;5;246m<chr>
[39m
[23m    
[3m
[38;5;246m<dbl>
[39m
[23m  
[3m
[38;5;246m<dbl>
[39m
[23m 
[3m
[38;5;246m<dbl>
[39m
[23m 
[3m
[38;5;246m<dbl>
[39m
[23m 
[3m
[38;5;246m<dbl>
[39m
[23m   
[3m
[38;5;246m<dbl>
[39m
[23m
-
[38;5;250m1
[39m mu        6.41   6.52  4.26 0.940  11.5 -
[31m10
[39m
[31m.
[39m
[31m7
[39m  
-
[38;5;250m2
[39m tau       5.25   4.42  3.65 1.47   10.3   0.516
    +
      variable     MEAN   median    My_SD      10%      90%    Minimum
+1       mu 6.421661 6.560445 3.994261 1.488318 11.54665 -15.442700
+2      tau 5.211890 4.447485 3.457118 1.488050 10.02499   0.822982

    Arguments to all summary functions can also be specified with .args.

     fit$summary(c("mu", "tau"), quantile, .args = list(probs = c(0.025, .05, .95, .975)))
    -
    
[38;5;246m# A tibble: 2 × 5
[39m
-  variable `2.5%`   `5%` `95%` `97.5%`
-  
[3m
[38;5;246m<chr>
[39m
[23m     
[3m
[38;5;246m<dbl>
[39m
[23m  
[3m
[38;5;246m<dbl>
[39m
[23m 
[3m
[38;5;246m<dbl>
[39m
[23m   
[3m
[38;5;246m<dbl>
[39m
[23m
-
[38;5;250m1
[39m mu       -
[31m2
[39m
[31m.
[39m
[31m32
[39m  -
[31m0
[39m
[31m.
[39m
[31m818
[39m  13.3    14.5
-
[38;5;250m2
[39m tau       0.760  1.05   12.3    14.2
    +
      variable      2.5%         5%      95%    97.5%
+1       mu -1.441798 -0.1207198 13.16044 14.63687
+2      tau  0.822982  1.0590100 12.09968 13.78430

    The summary functions are applied to the array of sample values, with dimension iter_samplingxchains.

     fit$summary(variables = NULL, dim, colMeans)
    -
    
[38;5;246m# A tibble: 11 × 7
[39m
-   variable dim.1 dim.2    `1`    `2`    `3`    `4`
-   
[3m
[38;5;246m<chr>
[39m
[23m    
[3m
[38;5;246m<dbl>
[39m
[23m 
[3m
[38;5;246m<dbl>
[39m
[23m  
[3m
[38;5;246m<dbl>
[39m
[23m  
[3m
[38;5;246m<dbl>
[39m
[23m  
[3m
[38;5;246m<dbl>
[39m
[23m  
[3m
[38;5;246m<dbl>
[39m
[23m
-
[38;5;250m 1
[39m lp__      
[4m1
[24m000     4 -
[31m57
[39m
[31m.
[39m
[31m8
[39m  -
[31m57
[39m
[31m.
[39m
[31m9
[39m  -
[31m59
[39m
[31m.
[39m
[31m0
[39m  -
[31m56
[39m
[31m.
[39m
[31m7
[39m 
-
[38;5;250m 2
[39m mu        
[4m1
[24m000     4   6.61   6.42   6.48   6.11
-
[38;5;250m 3
[39m tau       
[4m1
[24m000     4   4.94   5.36   5.89   4.83
-
[38;5;250m 4
[39m theta[1]  
[4m1
[24m000     4   9.42   9.27   9.92   8.65
-
[38;5;250m 5
[39m theta[2]  
[4m1
[24m000     4   7.07   6.65   6.95   6.47
-
[38;5;250m 6
[39m theta[3]  
[4m1
[24m000     4   5.54   5.17   5.47   4.86
-
[38;5;250m 7
[39m theta[4]  
[4m1
[24m000     4   6.57   6.50   6.67   6.28
-
[38;5;250m 8
[39m theta[5]  
[4m1
[24m000     4   4.87   4.55   4.50   4.67
-
[38;5;250m 9
[39m theta[6]  
[4m1
[24m000     4   5.64   5.56   5.43   5.18
-
[38;5;250m10
[39m theta[7]  
[4m1
[24m000     4   8.97   9.29   9.63   8.50
-
[38;5;250m11
[39m theta[8]  
[4m1
[24m000     4   7.00   7.16   7.37   6.73
    +
       variable dim.1 dim.2          1          2          3          4
+1      lp__  1000     4 -59.408601 -58.175641 -58.144473 -55.940741
+2        mu  1000     4   6.689529   6.327474   6.548562   6.121077
+3       tau  1000     4   6.015816   5.271415   5.222288   4.338039
+4  theta[1]  1000     4  10.309827   9.010128   9.089769   8.229576
+5  theta[2]  1000     4   6.940841   6.642238   6.917915   6.502333
+6  theta[3]  1000     4   5.378242   5.517793   5.881470   5.480988
+7  theta[4]  1000     4   6.732677   6.458448   6.658988   6.177763
+8  theta[5]  1000     4   4.460429   4.316417   4.822090   4.811620
+9  theta[6]  1000     4   5.567601   5.449356   5.743810   5.046207
+10 theta[7]  1000     4   9.872239   9.168575   9.143428   8.239549
+11 theta[8]  1000     4   6.985384   6.844136   7.146715   6.643711

    For this reason users may have unexpected results if they use stats::var() directly, as it will return a covariance matrix. An alternative is the distributional::variance() @@ -261,30 +263,26 @@

    Summary statisticsposterior::variance().

     fit$summary(c("mu", "tau"), posterior::variance, ~var(as.vector(.x)))
    -
    
[38;5;246m# A tibble: 2 × 3
[39m
-  variable `posterior::variance` `~var(as.vector(.x))`
-  
[3m
[38;5;246m<chr>
[39m
[23m                    
[3m
[38;5;246m<dbl>
[39m
[23m                 
[3m
[38;5;246m<dbl>
[39m
[23m
-
[38;5;250m1
[39m mu                        18.2                  18.2
-
[38;5;250m2
[39m tau                       13.3                  13.3
    +
      variable posterior::variance ~var(as.vector(.x))
+1       mu            15.95412            15.95412
+2      tau            11.95166            11.95166

    Summary functions need not be numeric, but these won’t work with $print().

     strict_pos <- function(x) if (all(x > 0)) "yes" else "no"
 fit$summary(variables = NULL, "Strictly Positive" = strict_pos)
    -
    
[38;5;246m# A tibble: 11 × 2
[39m
-   variable `Strictly Positive`
-   
[3m
[38;5;246m<chr>
[39m
[23m    
[3m
[38;5;246m<chr>
[39m
[23m              
-
[38;5;250m 1
[39m lp__     no                 
-
[38;5;250m 2
[39m mu       no                 
-
[38;5;250m 3
[39m tau      yes                
-
[38;5;250m 4
[39m theta[1] no                 
-
[38;5;250m 5
[39m theta[2] no                 
-
[38;5;250m 6
[39m theta[3] no                 
-
[38;5;250m 7
[39m theta[4] no                 
-
[38;5;250m 8
[39m theta[5] no                 
-
[38;5;250m 9
[39m theta[6] no                 
-
[38;5;250m10
[39m theta[7] no                 
-
[38;5;250m11
[39m theta[8] no
    +
       variable Strictly Positive
+1      lp__                no
+2        mu                no
+3       tau               yes
+4  theta[1]                no
+5  theta[2]                no
+6  theta[3]                no
+7  theta[4]                no
+8  theta[5]                no
+9  theta[6]                no
+10 theta[7]                no
+11 theta[8]                no
     # fit$print(variables = NULL, "Strictly Positive" = strict_pos)

    For more information, see posterior::summarise_draws(), @@ -304,7 +302,7 @@

    Extracting posterior draws/samples# iterations x chains x variables draws_arr <- fit$draws() # or format="array" str(draws_arr) -
     'draws_array' num [1:1000, 1:4, 1:11] -60.1 -61.2 -56.1 -57.3 -60.4 ...
+
     'draws_array' num [1:1000, 1:4, 1:11] -57.1 -58.5 -56 -57.2 -58.5 ...
  - attr(*, "dimnames")=List of 3
   ..$ iteration: chr [1:1000] "1" "2" "3" "4" ...
   ..$ chain    : chr [1:4] "1" "2" "3" "4"
@@ -314,34 +312,34 @@ 
    Extracting posterior draws/samplesdraws_df <- fit$draws(format = "df")
 str(draws_df)
    
 
    draws_df [4,000 × 14] (S3: draws_df/draws/tbl_df/tbl/data.frame)
- $ lp__      : num [1:4000] -60.1 -61.2 -56.1 -57.3 -60.4 ...
- $ mu        : num [1:4000] 2.61848 0.7849 8.10256 6.57364 -0.00287 ...
- $ tau       : num [1:4000] 5.08 7.18 4.03 3.89 7.07 ...
- $ theta[1]  : num [1:4000] 10.98 9.64 5.26 11.34 13.75 ...
- $ theta[2]  : num [1:4000] -2.54 -3.89 10.58 2.63 10.17 ...
- $ theta[3]  : num [1:4000] -4.73 -3.78 7.2 4.05 4.26 ...
- $ theta[4]  : num [1:4000] 5.14 10.67 7.18 4.7 2.94 ...
- $ theta[5]  : num [1:4000] -5.56 1.63 1.64 3.55 3.04 ...
- $ theta[6]  : num [1:4000] 1.153 3.155 12.283 -2.088 -0.133 ...
- $ theta[7]  : num [1:4000] 4.064 0.872 10.52 5.397 7.21 ...
- $ theta[8]  : num [1:4000] 8.65 -5.18 9.86 5.43 1.18 ...
+ $ lp__      : num [1:4000] -57.1 -58.5 -56 -57.2 -58.5 ...
+ $ mu        : num [1:4000] 9.17 4.62 11.15 5.67 5.37 ...
+ $ tau       : num [1:4000] 2.8 4.94 2.62 5.14 7.06 ...
+ $ theta[1]  : num [1:4000] 17.589 0.16 18.328 0.237 1.688 ...
+ $ theta[2]  : num [1:4000] 8.55 10.22 8.37 9.18 9.6 ...
+ $ theta[3]  : num [1:4000] 10.75 6.36 10.37 9.1 8.14 ...
+ $ theta[4]  : num [1:4000] 10.85 3.4 12.92 6.16 6.3 ...
+ $ theta[5]  : num [1:4000] 11 2.25 11.42 2.25 2.76 ...
+ $ theta[6]  : num [1:4000] 9.55 8.19 9.94 6.84 4.9 ...
+ $ theta[7]  : num [1:4000] 6.58 14.62 9.12 6.07 5.08 ...
+ $ theta[8]  : num [1:4000] 11.31 6.14 10.26 4.92 7.66 ...
  $ .chain    : int [1:4000] 1 1 1 1 1 1 1 1 1 1 ...
  $ .iteration: int [1:4000] 1 2 3 4 5 6 7 8 9 10 ...
  $ .draw     : int [1:4000] 1 2 3 4 5 6 7 8 9 10 ...
    
 
     print(draws_df)
    
 
    # A draws_df: 1000 iterations, 4 chains, and 11 variables
-   lp__      mu tau theta[1] theta[2] theta[3] theta[4] theta[5]
-1   -60  2.6185 5.1     11.0     -2.5     -4.7      5.1    -5.56
-2   -61  0.7849 7.2      9.6     -3.9     -3.8     10.7     1.63
-3   -56  8.1026 4.0      5.3     10.6      7.2      7.2     1.64
-4   -57  6.5736 3.9     11.3      2.6      4.1      4.7     3.55
-5   -60 -0.0029 7.1     13.7     10.2      4.3      2.9     3.04
-6   -59  3.7000 4.5     10.2      7.6     -2.3      1.2     2.20
-7   -62  5.0756 8.0     11.3     10.9      5.1     14.6     0.37
-8   -63 14.5222 6.3     19.5     16.0      8.3      2.0    18.21
-9   -58 11.0878 4.0     16.8     12.8     10.8      7.9     3.32
-10  -56 10.4559 4.2      9.6     15.2     12.4      8.2    10.41
+   lp__    mu tau theta[1] theta[2] theta[3] theta[4] theta[5]
+1   -57  9.17 2.8    17.59      8.5    10.75     10.8     11.0
+2   -58  4.62 4.9     0.16     10.2     6.36      3.4      2.3
+3   -56 11.15 2.6    18.33      8.4    10.37     12.9     11.4
+4   -57  5.67 5.1     0.24      9.2     9.10      6.2      2.3
+5   -59  5.37 7.1     1.69      9.6     8.14      6.3      2.8
+6   -59  0.64 4.4     6.20      6.6     0.44      4.1      2.4
+7   -53  7.85 3.0     6.58      6.7     8.34      7.4      6.7
+8   -54  8.05 3.9     7.66      6.5     4.54      7.1      8.3
+9   -54  6.80 3.3     7.75      8.5     1.37      6.6      9.4
+10  -59  5.09 5.8     0.40      9.4    -1.92     13.0      2.4
 # ... with 3990 more draws, and 3 more variables
 # ... hidden reserved variables {'.chain', '.iteration', '.draw'}
    
 
    To convert an existing draws object to a different format use the
diff --git a/vignettes/posterior.Rmd b/vignettes/posterior.Rmd
index 52617834..15da8e68 100644
--- a/vignettes/posterior.Rmd
+++ b/vignettes/posterior.Rmd
@@ -15,6 +15,14 @@ vignette: >
 ```{r child="children/_settings-knitr.Rmd"}
 ```
 
+```{r include=FALSE}
+# Needed temporarily to avoiding weird rendering of posterior's tibbles
+# in pkgdown sites
+print.tbl_df <- function(x, ...) {
+  print.data.frame(x)
+}
+```
+
 ## Summary statistics
 
 We can easily customize the summary statistics reported by `$summary()` and `$print()`.
@@ -45,7 +53,10 @@ fit$metadata()$model_params
 fit$summary(variables = NULL, "mean", "median")
 ```
 
-Summary functions can be specified by character string, function, or using a formula (or anything else supported by [rlang::as_function]). If these arguments are named, those names will be used in the tibble output. If the summary results are named they will take precedence.
+Summary functions can be specified by character string, function, or using a
+formula (or anything else supported by `rlang::as_function()`). If these
+arguments are named, those names will be used in the tibble output. If the
+summary results are named they will take precedence.
 ```{r}
 my_sd <- function(x) c(My_SD = sd(x))
 fit$summary(

From 0d0f307a04f707fe65cbd43860f364ce7d387d40 Mon Sep 17 00:00:00 2001
From: jgabry 
Date: Thu, 9 Nov 2023 12:21:41 -0700
Subject: [PATCH 09/19] add CONTRIBUTING.md based on tidyverse

---
 .github/CONTRIBUTING.md | 56 +++++++++++++++++++++++++++++++++++++++++
 1 file changed, 56 insertions(+)
 create mode 100644 .github/CONTRIBUTING.md

diff --git a/.github/CONTRIBUTING.md b/.github/CONTRIBUTING.md
new file mode 100644
index 00000000..f28c8b71
--- /dev/null
+++ b/.github/CONTRIBUTING.md
@@ -0,0 +1,56 @@
+# Contributing to cmdstanr
+
+This outlines how to propose a change to cmdstanr and is based on similar
+instructions for tidyverse packages, including the contributing guidelines
+generated by `usethis::use_tidy_contributing()`.
+
+## Fixing typos
+
+You can fix typos, spelling mistakes, or grammatical errors in the documentation
+directly using the GitHub web interface, as long as the changes are made in the
+_source_ file. This generally means you'll need to edit 
+[roxygen2 comments](https://roxygen2.r-lib.org/articles/roxygen2.html) in an `.R`, 
+not a `.Rd` file. You can find the `.R` file that generates the `.Rd` by reading
+the comment in the first line.
+
+## Bigger changes
+
+If you want to make a bigger change, it's a good idea to first file an issue and
+make sure someone from the team agrees that it’s needed. If you’ve found a bug,
+please file an issue that illustrates the bug with a minimal reproducible
+example (see e.g. the [tidyverse reprex instructions](https://www.tidyverse.org/help/#reprex)). 
+The tidyverse guide on [how to create a great issue](https://code-review.tidyverse.org/issues/) 
+has more advice.
+
+### Pull request process
+
+If you are new to creating pull requests here are some tips. Using the functions
+from the `usethis` package is not required but can be helpful if this process is
+new to you.
+
+*   Fork the package and clone onto your computer. If you haven't done this before, we recommend using `usethis::create_from_github("stan-dev/cmdstanr", fork = TRUE)`.
+
+*   Install all development dependencies with `devtools::install_dev_deps()`, and then make sure the package passes R CMD check by running `devtools::check()`. 
+    If R CMD check doesn't pass cleanly, it's a good idea to ask for help before continuing. 
+*   Create a Git branch for your pull request (PR). We recommend using `usethis::pr_init("brief-description-of-change")`.
+
+*   Make your changes, commit to git, and then create a PR by running `usethis::pr_push()`, and following the prompts in your browser.
+    The title of your PR should briefly describe the change.
+    The body of your PR should contain `Fixes #issue-number`.
+
+*  For user-facing changes, add a bullet to the top of `NEWS.md` (i.e. just below the first header). Follow the style already used in `NEWS.md`.
+
+### Code style
+
+*   New code should attempt to follow the style used in the package. When in doubt follow the tidyverse [style guide](https://style.tidyverse.org). 
+
+*  We use [roxygen2](https://cran.r-project.org/package=roxygen2), with [Markdown syntax](https://cran.r-project.org/web/packages/roxygen2/vignettes/rd-formatting.html), for documentation.  
+
+*  We use [testthat](https://cran.r-project.org/package=testthat) for unit tests. 
+   Contributions with test cases included are easier to accept.  
+
+## Code of Conduct
+
+Please note that the cmdstanr project follows the Stan project's 
+[Code of Conduct](https://discourse.mc-stan.org/t/announcing-our-new-stan-code-of-conduct/23764).
+By contributing to this project you agree to abide by its terms.

From 6844a6a8e4a3a681e42804c6926fff5f4f6cd03d Mon Sep 17 00:00:00 2001
From: jgabry 
Date: Fri, 10 Nov 2023 12:13:26 -0700
Subject: [PATCH 10/19] init_model_methods for pathfinder

closes #872
---
 R/model.R | 29 ++++++++++++++++-------------
 1 file changed, 16 insertions(+), 13 deletions(-)

diff --git a/R/model.R b/R/model.R
index f9a0e74d..d2bec312 100644
--- a/R/model.R
+++ b/R/model.R
@@ -1904,23 +1904,26 @@ pathfinder <- function(data = NULL,
     model_variables <- self$variables()
   }
   pathfinder_args <- PathfinderArgs$new(
-   init_alpha = init_alpha,
-   tol_obj = tol_obj,
-   tol_rel_obj = tol_rel_obj,
-   tol_grad = tol_grad,
-   tol_rel_grad = tol_rel_grad,
-   tol_param = tol_param,
-   history_size = history_size,
-   draws = draws,
-   single_path_draws = single_path_draws,
-   num_paths = num_paths,
-   max_lbfgs_iters = max_lbfgs_iters,
-   num_elbo_draws = num_elbo_draws,
-   save_single_paths = save_single_paths)
+    init_alpha = init_alpha,
+    tol_obj = tol_obj,
+    tol_rel_obj = tol_rel_obj,
+    tol_grad = tol_grad,
+    tol_rel_grad = tol_rel_grad,
+    tol_param = tol_param,
+    history_size = history_size,
+    draws = draws,
+    single_path_draws = single_path_draws,
+    num_paths = num_paths,
+    max_lbfgs_iters = max_lbfgs_iters,
+    num_elbo_draws = num_elbo_draws,
+    save_single_paths = save_single_paths
+  )
   args <- CmdStanArgs$new(
     method_args = pathfinder_args,
     stan_file = self$stan_file(),
     stan_code = suppressWarnings(self$code()),
+    model_methods_env = private$model_methods_env_,
+    standalone_env = self$functions,
     model_name = self$model_name(),
     exe_file = self$exe_file(),
     proc_ids = 1,

From a6f35c9b49a78ed65ed016e66255bd1c1d940cb1 Mon Sep 17 00:00:00 2001
From: jgabry 
Date: Fri, 10 Nov 2023 12:18:38 -0700
Subject: [PATCH 11/19] mention all model fitting methods in `init` doc

closes #874
---
 man-roxygen/model-common-args.R | 8 ++++----
 man/model-method-diagnose.Rd    | 8 ++++----
 man/model-method-laplace.Rd     | 8 ++++----
 man/model-method-optimize.Rd    | 8 ++++----
 man/model-method-pathfinder.Rd  | 8 ++++----
 man/model-method-sample.Rd      | 8 ++++----
 man/model-method-sample_mpi.Rd  | 8 ++++----
 man/model-method-variational.Rd | 8 ++++----
 8 files changed, 32 insertions(+), 32 deletions(-)

diff --git a/man-roxygen/model-common-args.R b/man-roxygen/model-common-args.R
index 85013821..7cdcbc49 100644
--- a/man-roxygen/model-common-args.R
+++ b/man-roxygen/model-common-args.R
@@ -34,10 +34,10 @@
 #'  [write_stan_json()] to write \R objects to JSON files compatible with
 #'  CmdStan.
 #'  * A list of lists containing initial values for all or some parameters. For
-#'  MCMC the list should contain a sublist for each chain. For optimization and
-#'  variational inference there should be just one sublist. The sublists should
-#'  have named elements corresponding to the parameters for which you are
-#'  specifying initial values. See **Examples**.
+#'  MCMC the list should contain a sublist for each chain. For other model
+#'  fitting methods there should be just one sublist. The sublists should have
+#'  named elements corresponding to the parameters for which you are specifying
+#'  initial values. See **Examples**.
 #'  * A function that returns a single list with names corresponding to the
 #'  parameters for which you are specifying initial values. The function can
 #'  take no arguments or a single argument `chain_id`. For MCMC, if the function
diff --git a/man/model-method-diagnose.Rd b/man/model-method-diagnose.Rd
index 413ad64b..dff6359c 100644
--- a/man/model-method-diagnose.Rd
+++ b/man/model-method-diagnose.Rd
@@ -51,10 +51,10 @@ containing initial values for all or some parameters. See
 \code{\link[=write_stan_json]{write_stan_json()}} to write \R objects to JSON files compatible with
 CmdStan.
 \item A list of lists containing initial values for all or some parameters. For
-MCMC the list should contain a sublist for each chain. For optimization and
-variational inference there should be just one sublist. The sublists should
-have named elements corresponding to the parameters for which you are
-specifying initial values. See \strong{Examples}.
+MCMC the list should contain a sublist for each chain. For other model
+fitting methods there should be just one sublist. The sublists should have
+named elements corresponding to the parameters for which you are specifying
+initial values. See \strong{Examples}.
 \item A function that returns a single list with names corresponding to the
 parameters for which you are specifying initial values. The function can
 take no arguments or a single argument \code{chain_id}. For MCMC, if the function
diff --git a/man/model-method-laplace.Rd b/man/model-method-laplace.Rd
index 76f343be..1b52763c 100644
--- a/man/model-method-laplace.Rd
+++ b/man/model-method-laplace.Rd
@@ -62,10 +62,10 @@ containing initial values for all or some parameters. See
 \code{\link[=write_stan_json]{write_stan_json()}} to write \R objects to JSON files compatible with
 CmdStan.
 \item A list of lists containing initial values for all or some parameters. For
-MCMC the list should contain a sublist for each chain. For optimization and
-variational inference there should be just one sublist. The sublists should
-have named elements corresponding to the parameters for which you are
-specifying initial values. See \strong{Examples}.
+MCMC the list should contain a sublist for each chain. For other model
+fitting methods there should be just one sublist. The sublists should have
+named elements corresponding to the parameters for which you are specifying
+initial values. See \strong{Examples}.
 \item A function that returns a single list with names corresponding to the
 parameters for which you are specifying initial values. The function can
 take no arguments or a single argument \code{chain_id}. For MCMC, if the function
diff --git a/man/model-method-optimize.Rd b/man/model-method-optimize.Rd
index 6ce6bfe1..23fc5e8c 100644
--- a/man/model-method-optimize.Rd
+++ b/man/model-method-optimize.Rd
@@ -68,10 +68,10 @@ containing initial values for all or some parameters. See
 \code{\link[=write_stan_json]{write_stan_json()}} to write \R objects to JSON files compatible with
 CmdStan.
 \item A list of lists containing initial values for all or some parameters. For
-MCMC the list should contain a sublist for each chain. For optimization and
-variational inference there should be just one sublist. The sublists should
-have named elements corresponding to the parameters for which you are
-specifying initial values. See \strong{Examples}.
+MCMC the list should contain a sublist for each chain. For other model
+fitting methods there should be just one sublist. The sublists should have
+named elements corresponding to the parameters for which you are specifying
+initial values. See \strong{Examples}.
 \item A function that returns a single list with names corresponding to the
 parameters for which you are specifying initial values. The function can
 take no arguments or a single argument \code{chain_id}. For MCMC, if the function
diff --git a/man/model-method-pathfinder.Rd b/man/model-method-pathfinder.Rd
index 3bc00d7a..6a05aa46 100644
--- a/man/model-method-pathfinder.Rd
+++ b/man/model-method-pathfinder.Rd
@@ -71,10 +71,10 @@ containing initial values for all or some parameters. See
 \code{\link[=write_stan_json]{write_stan_json()}} to write \R objects to JSON files compatible with
 CmdStan.
 \item A list of lists containing initial values for all or some parameters. For
-MCMC the list should contain a sublist for each chain. For optimization and
-variational inference there should be just one sublist. The sublists should
-have named elements corresponding to the parameters for which you are
-specifying initial values. See \strong{Examples}.
+MCMC the list should contain a sublist for each chain. For other model
+fitting methods there should be just one sublist. The sublists should have
+named elements corresponding to the parameters for which you are specifying
+initial values. See \strong{Examples}.
 \item A function that returns a single list with names corresponding to the
 parameters for which you are specifying initial values. The function can
 take no arguments or a single argument \code{chain_id}. For MCMC, if the function
diff --git a/man/model-method-sample.Rd b/man/model-method-sample.Rd
index ca42f1b8..a5bec867 100644
--- a/man/model-method-sample.Rd
+++ b/man/model-method-sample.Rd
@@ -88,10 +88,10 @@ containing initial values for all or some parameters. See
 \code{\link[=write_stan_json]{write_stan_json()}} to write \R objects to JSON files compatible with
 CmdStan.
 \item A list of lists containing initial values for all or some parameters. For
-MCMC the list should contain a sublist for each chain. For optimization and
-variational inference there should be just one sublist. The sublists should
-have named elements corresponding to the parameters for which you are
-specifying initial values. See \strong{Examples}.
+MCMC the list should contain a sublist for each chain. For other model
+fitting methods there should be just one sublist. The sublists should have
+named elements corresponding to the parameters for which you are specifying
+initial values. See \strong{Examples}.
 \item A function that returns a single list with names corresponding to the
 parameters for which you are specifying initial values. The function can
 take no arguments or a single argument \code{chain_id}. For MCMC, if the function
diff --git a/man/model-method-sample_mpi.Rd b/man/model-method-sample_mpi.Rd
index 1ffcbda5..742420d0 100644
--- a/man/model-method-sample_mpi.Rd
+++ b/man/model-method-sample_mpi.Rd
@@ -87,10 +87,10 @@ containing initial values for all or some parameters. See
 \code{\link[=write_stan_json]{write_stan_json()}} to write \R objects to JSON files compatible with
 CmdStan.
 \item A list of lists containing initial values for all or some parameters. For
-MCMC the list should contain a sublist for each chain. For optimization and
-variational inference there should be just one sublist. The sublists should
-have named elements corresponding to the parameters for which you are
-specifying initial values. See \strong{Examples}.
+MCMC the list should contain a sublist for each chain. For other model
+fitting methods there should be just one sublist. The sublists should have
+named elements corresponding to the parameters for which you are specifying
+initial values. See \strong{Examples}.
 \item A function that returns a single list with names corresponding to the
 parameters for which you are specifying initial values. The function can
 take no arguments or a single argument \code{chain_id}. For MCMC, if the function
diff --git a/man/model-method-variational.Rd b/man/model-method-variational.Rd
index 019da21d..94aca6c7 100644
--- a/man/model-method-variational.Rd
+++ b/man/model-method-variational.Rd
@@ -69,10 +69,10 @@ containing initial values for all or some parameters. See
 \code{\link[=write_stan_json]{write_stan_json()}} to write \R objects to JSON files compatible with
 CmdStan.
 \item A list of lists containing initial values for all or some parameters. For
-MCMC the list should contain a sublist for each chain. For optimization and
-variational inference there should be just one sublist. The sublists should
-have named elements corresponding to the parameters for which you are
-specifying initial values. See \strong{Examples}.
+MCMC the list should contain a sublist for each chain. For other model
+fitting methods there should be just one sublist. The sublists should have
+named elements corresponding to the parameters for which you are specifying
+initial values. See \strong{Examples}.
 \item A function that returns a single list with names corresponding to the
 parameters for which you are specifying initial values. The function can
 take no arguments or a single argument \code{chain_id}. For MCMC, if the function

From 71efb26e87f109988b4f0f74c08b1ddec82a5542 Mon Sep 17 00:00:00 2001
From: Aki Vehtari 
Date: Fri, 10 Nov 2023 21:44:45 +0200
Subject: [PATCH 12/19] Improve Pathfinder doc

---
 R/model.R | 24 ++++++++++++++++--------
 1 file changed, 16 insertions(+), 8 deletions(-)

diff --git a/R/model.R b/R/model.R
index d2bec312..49f7a9c0 100644
--- a/R/model.R
+++ b/R/model.R
@@ -93,7 +93,7 @@
 #' fit_optim <- mod$optimize(data = my_data_file, seed = 123)
 #' fit_optim$summary()
 #'
-#' # Run 'optimize' again with 'jacobian=TRUE' and then draw from laplace approximation
+#' # Run 'optimize' again with 'jacobian=TRUE' and then draw from Laplace approximation
 #' # to the posterior
 #' fit_optim <- mod$optimize(data = my_data_file, jacobian = TRUE)
 #' fit_laplace <- mod$laplace(data = my_data_file, mode = fit_optim, draws = 2000)
@@ -109,6 +109,11 @@
 #' fit_pf$summary()
 #' mcmc_hist(fit_pf$draws("theta"))
 #'
+#' # Run 'pathfinder' again with more paths, less draws, better covariance approximation,
+#' # and less LBFGSs iterations
+#' fit_pf <- mod$pathfinder(data = stan_data, num_paths=10, single_path_draws=40,
+#'                          history_size=50, max_lbfgs_iters=100)
+#'
 #' # Specifying initial values as a function
 #' fit_mcmc_w_init_fun <- mod$sample(
 #'   data = stan_data,
@@ -1539,7 +1544,7 @@ optimize <- function(data = NULL,
 CmdStanModel$set("public", name = "optimize", value = optimize)
 
 
-#' Run Stan's laplace algorithm
+#' Run Stan's Laplace algorithm
 #'
 #' @name model-method-laplace
 #' @aliases laplace
@@ -1828,12 +1833,15 @@ CmdStanModel$set("public", name = "variational", value = variational)
 #' @description The `$pathfinder()` method of a [`CmdStanModel`] object runs
 #'   Stan's Pathfinder algorithms. Pathfinder is a variational method for
 #'   approximately sampling from differentiable log densities. Starting from a
-#'   random initialization, Pathfinder locates normal approximations to the
-#'   target density along a quasi-Newton optimization path, with local
-#'   covariance estimated using the negative inverse Hessian estimates produced
-#'   by the L-BFGS optimizer. Pathfinder returns draws from the Gaussian
-#'   approximation with the lowest estimated Kullback-Leibler (KL) divergence to
-#'   the true posterior. See the
+#'   random initialization, Pathfinder locates normal approximations
+#'   to the target density along a quasi-Newton optimization path in
+#'   the unconstrained space, with local covariance estimated using
+#'   the negative inverse Hessian estimates produced by the LBFGS
+#'   optimizer. Pathfinder selects the normal approximation with the
+#'   lowest estimated Kullback-Leibler (KL) divergence to the true
+#'   posterior. Finally Pathfinder draws from that normal
+#'   approximation and returns the draws transformed to the
+#'   constrained scale. See the
 #'   [CmdStan User’s Guide](https://mc-stan.org/docs/cmdstan-guide/)
 #'   for more details.
 #'

From cf758861fae4fe88dba7f99013f019482f24fa14 Mon Sep 17 00:00:00 2001
From: jgabry 
Date: Fri, 10 Nov 2023 12:50:30 -0700
Subject: [PATCH 13/19] minor edit

---
 R/model.R                       |  4 ++--
 man/CmdStanModel.Rd             |  7 ++++++-
 man/cmdstan_model.Rd            |  7 ++++++-
 man/cmdstanr-package.Rd         |  7 ++++++-
 man/model-method-laplace.Rd     |  2 +-
 man/model-method-optimize.Rd    |  7 ++++++-
 man/model-method-pathfinder.Rd  | 22 +++++++++++++++-------
 man/model-method-sample.Rd      |  7 ++++++-
 man/model-method-variational.Rd |  7 ++++++-
 9 files changed, 54 insertions(+), 16 deletions(-)

diff --git a/R/model.R b/R/model.R
index 49f7a9c0..4b22b73b 100644
--- a/R/model.R
+++ b/R/model.R
@@ -109,8 +109,8 @@
 #' fit_pf$summary()
 #' mcmc_hist(fit_pf$draws("theta"))
 #'
-#' # Run 'pathfinder' again with more paths, less draws, better covariance approximation,
-#' # and less LBFGSs iterations
+#' # Run 'pathfinder' again with more paths, fewer draws per path,
+#' # better covariance approximation, and fewer LBFGSs iterations
 #' fit_pf <- mod$pathfinder(data = stan_data, num_paths=10, single_path_draws=40,
 #'                          history_size=50, max_lbfgs_iters=100)
 #'
diff --git a/man/CmdStanModel.Rd b/man/CmdStanModel.Rd
index 7f2c1f9f..55200357 100644
--- a/man/CmdStanModel.Rd
+++ b/man/CmdStanModel.Rd
@@ -112,7 +112,7 @@ my_data_file <- file.path(cmdstan_path(), "examples/bernoulli/bernoulli.data.jso
 fit_optim <- mod$optimize(data = my_data_file, seed = 123)
 fit_optim$summary()
 
-# Run 'optimize' again with 'jacobian=TRUE' and then draw from laplace approximation
+# Run 'optimize' again with 'jacobian=TRUE' and then draw from Laplace approximation
 # to the posterior
 fit_optim <- mod$optimize(data = my_data_file, jacobian = TRUE)
 fit_laplace <- mod$laplace(data = my_data_file, mode = fit_optim, draws = 2000)
@@ -128,6 +128,11 @@ fit_pf <- mod$pathfinder(data = stan_data, seed = 123)
 fit_pf$summary()
 mcmc_hist(fit_pf$draws("theta"))
 
+# Run 'pathfinder' again with more paths, fewer draws per path,
+# better covariance approximation, and fewer LBFGSs iterations
+fit_pf <- mod$pathfinder(data = stan_data, num_paths=10, single_path_draws=40,
+                         history_size=50, max_lbfgs_iters=100)
+
 # Specifying initial values as a function
 fit_mcmc_w_init_fun <- mod$sample(
   data = stan_data,
diff --git a/man/cmdstan_model.Rd b/man/cmdstan_model.Rd
index 8ab7139a..26af0c1d 100644
--- a/man/cmdstan_model.Rd
+++ b/man/cmdstan_model.Rd
@@ -98,7 +98,7 @@ my_data_file <- file.path(cmdstan_path(), "examples/bernoulli/bernoulli.data.jso
 fit_optim <- mod$optimize(data = my_data_file, seed = 123)
 fit_optim$summary()
 
-# Run 'optimize' again with 'jacobian=TRUE' and then draw from laplace approximation
+# Run 'optimize' again with 'jacobian=TRUE' and then draw from Laplace approximation
 # to the posterior
 fit_optim <- mod$optimize(data = my_data_file, jacobian = TRUE)
 fit_laplace <- mod$laplace(data = my_data_file, mode = fit_optim, draws = 2000)
@@ -114,6 +114,11 @@ fit_pf <- mod$pathfinder(data = stan_data, seed = 123)
 fit_pf$summary()
 mcmc_hist(fit_pf$draws("theta"))
 
+# Run 'pathfinder' again with more paths, fewer draws per path,
+# better covariance approximation, and fewer LBFGSs iterations
+fit_pf <- mod$pathfinder(data = stan_data, num_paths=10, single_path_draws=40,
+                         history_size=50, max_lbfgs_iters=100)
+
 # Specifying initial values as a function
 fit_mcmc_w_init_fun <- mod$sample(
   data = stan_data,
diff --git a/man/cmdstanr-package.Rd b/man/cmdstanr-package.Rd
index cabe3d8d..930105ee 100644
--- a/man/cmdstanr-package.Rd
+++ b/man/cmdstanr-package.Rd
@@ -125,7 +125,7 @@ my_data_file <- file.path(cmdstan_path(), "examples/bernoulli/bernoulli.data.jso
 fit_optim <- mod$optimize(data = my_data_file, seed = 123)
 fit_optim$summary()
 
-# Run 'optimize' again with 'jacobian=TRUE' and then draw from laplace approximation
+# Run 'optimize' again with 'jacobian=TRUE' and then draw from Laplace approximation
 # to the posterior
 fit_optim <- mod$optimize(data = my_data_file, jacobian = TRUE)
 fit_laplace <- mod$laplace(data = my_data_file, mode = fit_optim, draws = 2000)
@@ -141,6 +141,11 @@ fit_pf <- mod$pathfinder(data = stan_data, seed = 123)
 fit_pf$summary()
 mcmc_hist(fit_pf$draws("theta"))
 
+# Run 'pathfinder' again with more paths, fewer draws per path,
+# better covariance approximation, and fewer LBFGSs iterations
+fit_pf <- mod$pathfinder(data = stan_data, num_paths=10, single_path_draws=40,
+                         history_size=50, max_lbfgs_iters=100)
+
 # Specifying initial values as a function
 fit_mcmc_w_init_fun <- mod$sample(
   data = stan_data,
diff --git a/man/model-method-laplace.Rd b/man/model-method-laplace.Rd
index 1b52763c..64852f88 100644
--- a/man/model-method-laplace.Rd
+++ b/man/model-method-laplace.Rd
@@ -3,7 +3,7 @@
 \name{model-method-laplace}
 \alias{model-method-laplace}
 \alias{laplace}
-\title{Run Stan's laplace algorithm}
+\title{Run Stan's Laplace algorithm}
 \usage{
 laplace(
   data = NULL,
diff --git a/man/model-method-optimize.Rd b/man/model-method-optimize.Rd
index 23fc5e8c..88d40279 100644
--- a/man/model-method-optimize.Rd
+++ b/man/model-method-optimize.Rd
@@ -233,7 +233,7 @@ my_data_file <- file.path(cmdstan_path(), "examples/bernoulli/bernoulli.data.jso
 fit_optim <- mod$optimize(data = my_data_file, seed = 123)
 fit_optim$summary()
 
-# Run 'optimize' again with 'jacobian=TRUE' and then draw from laplace approximation
+# Run 'optimize' again with 'jacobian=TRUE' and then draw from Laplace approximation
 # to the posterior
 fit_optim <- mod$optimize(data = my_data_file, jacobian = TRUE)
 fit_laplace <- mod$laplace(data = my_data_file, mode = fit_optim, draws = 2000)
@@ -249,6 +249,11 @@ fit_pf <- mod$pathfinder(data = stan_data, seed = 123)
 fit_pf$summary()
 mcmc_hist(fit_pf$draws("theta"))
 
+# Run 'pathfinder' again with more paths, fewer draws per path,
+# better covariance approximation, and fewer LBFGSs iterations
+fit_pf <- mod$pathfinder(data = stan_data, num_paths=10, single_path_draws=40,
+                         history_size=50, max_lbfgs_iters=100)
+
 # Specifying initial values as a function
 fit_mcmc_w_init_fun <- mod$sample(
   data = stan_data,
diff --git a/man/model-method-pathfinder.Rd b/man/model-method-pathfinder.Rd
index 6a05aa46..283a9a32 100644
--- a/man/model-method-pathfinder.Rd
+++ b/man/model-method-pathfinder.Rd
@@ -171,12 +171,15 @@ A \code{\link{CmdStanPathfinder}} object.
 The \verb{$pathfinder()} method of a \code{\link{CmdStanModel}} object runs
 Stan's Pathfinder algorithms. Pathfinder is a variational method for
 approximately sampling from differentiable log densities. Starting from a
-random initialization, Pathfinder locates normal approximations to the
-target density along a quasi-Newton optimization path, with local
-covariance estimated using the negative inverse Hessian estimates produced
-by the L-BFGS optimizer. Pathfinder returns draws from the Gaussian
-approximation with the lowest estimated Kullback-Leibler (KL) divergence to
-the true posterior. See the
+random initialization, Pathfinder locates normal approximations
+to the target density along a quasi-Newton optimization path in
+the unconstrained space, with local covariance estimated using
+the negative inverse Hessian estimates produced by the LBFGS
+optimizer. Pathfinder selects the normal approximation with the
+lowest estimated Kullback-Leibler (KL) divergence to the true
+posterior. Finally Pathfinder draws from that normal
+approximation and returns the draws transformed to the
+constrained scale. See the
 \href{https://mc-stan.org/docs/cmdstan-guide/}{CmdStan User’s Guide}
 for more details.
 
@@ -241,7 +244,7 @@ my_data_file <- file.path(cmdstan_path(), "examples/bernoulli/bernoulli.data.jso
 fit_optim <- mod$optimize(data = my_data_file, seed = 123)
 fit_optim$summary()
 
-# Run 'optimize' again with 'jacobian=TRUE' and then draw from laplace approximation
+# Run 'optimize' again with 'jacobian=TRUE' and then draw from Laplace approximation
 # to the posterior
 fit_optim <- mod$optimize(data = my_data_file, jacobian = TRUE)
 fit_laplace <- mod$laplace(data = my_data_file, mode = fit_optim, draws = 2000)
@@ -257,6 +260,11 @@ fit_pf <- mod$pathfinder(data = stan_data, seed = 123)
 fit_pf$summary()
 mcmc_hist(fit_pf$draws("theta"))
 
+# Run 'pathfinder' again with more paths, fewer draws per path,
+# better covariance approximation, and fewer LBFGSs iterations
+fit_pf <- mod$pathfinder(data = stan_data, num_paths=10, single_path_draws=40,
+                         history_size=50, max_lbfgs_iters=100)
+
 # Specifying initial values as a function
 fit_mcmc_w_init_fun <- mod$sample(
   data = stan_data,
diff --git a/man/model-method-sample.Rd b/man/model-method-sample.Rd
index a5bec867..c3f2c1e6 100644
--- a/man/model-method-sample.Rd
+++ b/man/model-method-sample.Rd
@@ -342,7 +342,7 @@ my_data_file <- file.path(cmdstan_path(), "examples/bernoulli/bernoulli.data.jso
 fit_optim <- mod$optimize(data = my_data_file, seed = 123)
 fit_optim$summary()
 
-# Run 'optimize' again with 'jacobian=TRUE' and then draw from laplace approximation
+# Run 'optimize' again with 'jacobian=TRUE' and then draw from Laplace approximation
 # to the posterior
 fit_optim <- mod$optimize(data = my_data_file, jacobian = TRUE)
 fit_laplace <- mod$laplace(data = my_data_file, mode = fit_optim, draws = 2000)
@@ -358,6 +358,11 @@ fit_pf <- mod$pathfinder(data = stan_data, seed = 123)
 fit_pf$summary()
 mcmc_hist(fit_pf$draws("theta"))
 
+# Run 'pathfinder' again with more paths, fewer draws per path,
+# better covariance approximation, and fewer LBFGSs iterations
+fit_pf <- mod$pathfinder(data = stan_data, num_paths=10, single_path_draws=40,
+                         history_size=50, max_lbfgs_iters=100)
+
 # Specifying initial values as a function
 fit_mcmc_w_init_fun <- mod$sample(
   data = stan_data,
diff --git a/man/model-method-variational.Rd b/man/model-method-variational.Rd
index 94aca6c7..e7d9fa1e 100644
--- a/man/model-method-variational.Rd
+++ b/man/model-method-variational.Rd
@@ -233,7 +233,7 @@ my_data_file <- file.path(cmdstan_path(), "examples/bernoulli/bernoulli.data.jso
 fit_optim <- mod$optimize(data = my_data_file, seed = 123)
 fit_optim$summary()
 
-# Run 'optimize' again with 'jacobian=TRUE' and then draw from laplace approximation
+# Run 'optimize' again with 'jacobian=TRUE' and then draw from Laplace approximation
 # to the posterior
 fit_optim <- mod$optimize(data = my_data_file, jacobian = TRUE)
 fit_laplace <- mod$laplace(data = my_data_file, mode = fit_optim, draws = 2000)
@@ -249,6 +249,11 @@ fit_pf <- mod$pathfinder(data = stan_data, seed = 123)
 fit_pf$summary()
 mcmc_hist(fit_pf$draws("theta"))
 
+# Run 'pathfinder' again with more paths, fewer draws per path,
+# better covariance approximation, and fewer LBFGSs iterations
+fit_pf <- mod$pathfinder(data = stan_data, num_paths=10, single_path_draws=40,
+                         history_size=50, max_lbfgs_iters=100)
+
 # Specifying initial values as a function
 fit_mcmc_w_init_fun <- mod$sample(
   data = stan_data,

From d4c854fd00e276b2034be891983df8440170f7e2 Mon Sep 17 00:00:00 2001
From: jgabry 
Date: Fri, 17 Nov 2023 12:45:48 -0700
Subject: [PATCH 14/19] jacobian_adjustment -> jacobian

closes #877
---
 R/fit.R                             | 47 ++++++++++++++++-------------
 man/fit-method-grad_log_prob.Rd     | 15 ++++++---
 man/fit-method-hessian.Rd           | 11 ++++---
 man/fit-method-log_prob.Rd          | 14 +++++----
 tests/testthat/test-model-methods.R |  2 +-
 5 files changed, 51 insertions(+), 38 deletions(-)

diff --git a/R/fit.R b/R/fit.R
index 164e420e..b16b260f 100644
--- a/R/fit.R
+++ b/R/fit.R
@@ -362,12 +362,13 @@ CmdStanFit$set("public", name = "init_model_methods", value = init_model_methods
 #'
 #' @name fit-method-log_prob
 #' @aliases log_prob
-#' @description The `$log_prob()` method provides access to the Stan model's `log_prob` function
+#' @description The `$log_prob()` method provides access to the Stan model's
+#'   `log_prob` function.
 #'
-#' @param unconstrained_variables (numeric) A vector of unconstrained parameters
-#'   to be passed to `log_prob`.
-#' @param jacobian_adjustment (logical) Whether to include the log-density
-#'   adjustments from un/constraining variables.
+#' @param unconstrained_variables (numeric) A vector of unconstrained parameters.
+#' @param jacobian (logical) Whether to include the log-density adjustments from
+#'   un/constraining variables.
+#' @param jacobian_adjustment Deprecated. Please use `jacobian` instead.
 #'
 #' @examples
 #' \dontrun{
@@ -380,7 +381,11 @@ CmdStanFit$set("public", name = "init_model_methods", value = init_model_methods
 #'   [unconstrain_variables()], [unconstrain_draws()], [variable_skeleton()],
 #'   [hessian()]
 #'
-log_prob <- function(unconstrained_variables, jacobian_adjustment = TRUE) {
+log_prob <- function(unconstrained_variables, jacobian = TRUE, jacobian_adjustment = NULL) {
+  if (!is.null(jacobian_adjustment)) {
+    warning("'jacobian_adjustment' is deprecated. Please use 'jacobian' instead.", call. = FALSE)
+    jacobian <- jacobian_adjustment
+  }
   if (is.null(private$model_methods_env_$model_ptr)) {
     stop("The method has not been compiled, please call `init_model_methods()` first",
         call. = FALSE)
@@ -390,7 +395,7 @@ log_prob <- function(unconstrained_variables, jacobian_adjustment = TRUE) {
           length(unconstrained_variables), " were provided!", call. = FALSE)
   }
   private$model_methods_env_$log_prob(private$model_methods_env_$model_ptr_,
-                                      unconstrained_variables, jacobian_adjustment)
+                                      unconstrained_variables, jacobian)
 }
 CmdStanFit$set("public", name = "log_prob", value = log_prob)
 
@@ -401,11 +406,7 @@ CmdStanFit$set("public", name = "log_prob", value = log_prob)
 #' @aliases grad_log_prob
 #' @description The `$grad_log_prob()` method provides access to the Stan
 #'   model's `log_prob` function and its derivative.
-#'
-#' @param unconstrained_variables (numeric) A vector of unconstrained parameters
-#'   to be passed to `grad_log_prob`.
-#' @param jacobian_adjustment (logical) Whether to include the log-density
-#'   adjustments from un/constraining variables.
+#' @inheritParams fit-method-log_prob
 #'
 #' @examples
 #' \dontrun{
@@ -418,7 +419,11 @@ CmdStanFit$set("public", name = "log_prob", value = log_prob)
 #'   [unconstrain_variables()], [unconstrain_draws()], [variable_skeleton()],
 #'   [hessian()]
 #'
-grad_log_prob <- function(unconstrained_variables, jacobian_adjustment = TRUE) {
+grad_log_prob <- function(unconstrained_variables, jacobian = TRUE, jacobian_adjustment = NULL) {
+  if (!is.null(jacobian_adjustment)) {
+    warning("'jacobian_adjustment' is deprecated. Please use 'jacobian' instead.", call. = FALSE)
+    jacobian <- jacobian_adjustment
+  }
   if (is.null(private$model_methods_env_$model_ptr)) {
     stop("The method has not been compiled, please call `init_model_methods()` first",
         call. = FALSE)
@@ -428,7 +433,7 @@ grad_log_prob <- function(unconstrained_variables, jacobian_adjustment = TRUE) {
           length(unconstrained_variables), " were provided!", call. = FALSE)
   }
   private$model_methods_env_$grad_log_prob(private$model_methods_env_$model_ptr_,
-                                            unconstrained_variables, jacobian_adjustment)
+                                            unconstrained_variables, jacobian)
 }
 CmdStanFit$set("public", name = "grad_log_prob", value = grad_log_prob)
 
@@ -439,11 +444,7 @@ CmdStanFit$set("public", name = "grad_log_prob", value = grad_log_prob)
 #' @aliases hessian
 #' @description The `$hessian()` method provides access to the Stan model's
 #'   `log_prob`, its derivative, and its hessian.
-#'
-#' @param unconstrained_variables (numeric) A vector of unconstrained parameters
-#'   to be passed to `hessian`.
-#' @param jacobian_adjustment (logical) Whether to include the log-density
-#'   adjustments from un/constraining variables.
+#' @inheritParams fit-method-log_prob
 #'
 #' @examples
 #' \dontrun{
@@ -456,7 +457,11 @@ CmdStanFit$set("public", name = "grad_log_prob", value = grad_log_prob)
 #'   [unconstrain_variables()], [unconstrain_draws()], [variable_skeleton()],
 #'   [hessian()]
 #'
-hessian <- function(unconstrained_variables, jacobian_adjustment = TRUE) {
+hessian <- function(unconstrained_variables, jacobian = TRUE, jacobian_adjustment = TRUE) {
+  if (!is.null(jacobian_adjustment)) {
+    warning("'jacobian_adjustment' is deprecated. Please use 'jacobian' instead.", call. = FALSE)
+    jacobian <- jacobian_adjustment
+  }
   if (is.null(private$model_methods_env_$model_ptr)) {
     stop("The method has not been compiled, please call `init_model_methods()` first",
         call. = FALSE)
@@ -466,7 +471,7 @@ hessian <- function(unconstrained_variables, jacobian_adjustment = TRUE) {
           length(unconstrained_variables), " were provided!", call. = FALSE)
   }
   private$model_methods_env_$hessian(private$model_methods_env_$model_ptr_,
-                                      unconstrained_variables, jacobian_adjustment)
+                                      unconstrained_variables, jacobian)
 }
 CmdStanFit$set("public", name = "hessian", value = hessian)
 
diff --git a/man/fit-method-grad_log_prob.Rd b/man/fit-method-grad_log_prob.Rd
index 42b61046..b74f19ba 100644
--- a/man/fit-method-grad_log_prob.Rd
+++ b/man/fit-method-grad_log_prob.Rd
@@ -6,14 +6,19 @@
 \title{Calculate the log-probability and the gradient w.r.t. each input for a
 given vector of unconstrained parameters}
 \usage{
-grad_log_prob(unconstrained_variables, jacobian_adjustment = TRUE)
+grad_log_prob(
+  unconstrained_variables,
+  jacobian = TRUE,
+  jacobian_adjustment = NULL
+)
 }
 \arguments{
-\item{unconstrained_variables}{(numeric) A vector of unconstrained parameters
-to be passed to \code{grad_log_prob}.}
+\item{unconstrained_variables}{(numeric) A vector of unconstrained parameters.}
 
-\item{jacobian_adjustment}{(logical) Whether to include the log-density
-adjustments from un/constraining variables.}
+\item{jacobian}{(logical) Whether to include the log-density adjustments from
+un/constraining variables.}
+
+\item{jacobian_adjustment}{Deprecated. Please use \code{jacobian} instead.}
 }
 \description{
 The \verb{$grad_log_prob()} method provides access to the Stan
diff --git a/man/fit-method-hessian.Rd b/man/fit-method-hessian.Rd
index 8e1d5f30..9dd54830 100644
--- a/man/fit-method-hessian.Rd
+++ b/man/fit-method-hessian.Rd
@@ -6,14 +6,15 @@
 \title{Calculate the log-probability , the gradient w.r.t. each input, and the hessian
 for a given vector of unconstrained parameters}
 \usage{
-hessian(unconstrained_variables, jacobian_adjustment = TRUE)
+hessian(unconstrained_variables, jacobian = TRUE, jacobian_adjustment = TRUE)
 }
 \arguments{
-\item{unconstrained_variables}{(numeric) A vector of unconstrained parameters
-to be passed to \code{hessian}.}
+\item{unconstrained_variables}{(numeric) A vector of unconstrained parameters.}
 
-\item{jacobian_adjustment}{(logical) Whether to include the log-density
-adjustments from un/constraining variables.}
+\item{jacobian}{(logical) Whether to include the log-density adjustments from
+un/constraining variables.}
+
+\item{jacobian_adjustment}{Deprecated. Please use \code{jacobian} instead.}
 }
 \description{
 The \verb{$hessian()} method provides access to the Stan model's
diff --git a/man/fit-method-log_prob.Rd b/man/fit-method-log_prob.Rd
index 46eb4fb6..41edbc3c 100644
--- a/man/fit-method-log_prob.Rd
+++ b/man/fit-method-log_prob.Rd
@@ -5,17 +5,19 @@
 \alias{log_prob}
 \title{Calculate the log-probability given a provided vector of unconstrained parameters.}
 \usage{
-log_prob(unconstrained_variables, jacobian_adjustment = TRUE)
+log_prob(unconstrained_variables, jacobian = TRUE, jacobian_adjustment = NULL)
 }
 \arguments{
-\item{unconstrained_variables}{(numeric) A vector of unconstrained parameters
-to be passed to \code{log_prob}.}
+\item{unconstrained_variables}{(numeric) A vector of unconstrained parameters.}
 
-\item{jacobian_adjustment}{(logical) Whether to include the log-density
-adjustments from un/constraining variables.}
+\item{jacobian}{(logical) Whether to include the log-density adjustments from
+un/constraining variables.}
+
+\item{jacobian_adjustment}{Deprecated. Please use \code{jacobian} instead.}
 }
 \description{
-The \verb{$log_prob()} method provides access to the Stan model's \code{log_prob} function
+The \verb{$log_prob()} method provides access to the Stan model's
+\code{log_prob} function.
 }
 \examples{
 \dontrun{
diff --git a/tests/testthat/test-model-methods.R b/tests/testthat/test-model-methods.R
index 1289cb06..c7879288 100644
--- a/tests/testthat/test-model-methods.R
+++ b/tests/testthat/test-model-methods.R
@@ -65,7 +65,7 @@ test_that("Methods return correct values", {
     hessian = as.matrix(-2.4937604019289194568, nrow=1, ncol=1)
   )
 
-  expect_equal(fit$hessian(unconstrained_variables=c(0.1), jacobian_adjustment = FALSE),
+  expect_equal(fit$hessian(unconstrained_variables=c(0.1), jacobian = FALSE),
                hessian_noadj)
 
   cpars <- fit$constrain_variables(c(0.1))

From 5b92ba04d806998f4454a96651c73ec74f128e58 Mon Sep 17 00:00:00 2001
From: jgabry 
Date: Wed, 13 Dec 2023 09:00:03 -0700
Subject: [PATCH 15/19] update pathfinder 'draws' doc

advances #878
---
 R/model.R                      | 5 +++--
 man/model-method-pathfinder.Rd | 5 +++--
 2 files changed, 6 insertions(+), 4 deletions(-)

diff --git a/R/model.R b/R/model.R
index 4b22b73b..0ea2132d 100644
--- a/R/model.R
+++ b/R/model.R
@@ -1865,8 +1865,9 @@ CmdStanModel$set("public", name = "variational", value = variational)
 #'   pathfinder should return. The number of draws PSIS sampling samples from
 #'   will be equal to `single_path_draws * num_paths`.
 #' @param draws (positive integer) Number of draws to return after performing
-#'   pareto smooted importance sampling (PSIS). This must be smaller than
-#'   `single_path_draws * num_paths`.
+#'   pareto smooted importance sampling (PSIS). This should be smaller than
+#'   `single_path_draws * num_paths` (future versions of CmdStan will throw a
+#'   warning).
 #' @param num_paths (positive integer) Number of single pathfinders to run.
 #' @param max_lbfgs_iters (positive integer) The maximum number of iterations
 #'   for LBFGS.
diff --git a/man/model-method-pathfinder.Rd b/man/model-method-pathfinder.Rd
index 283a9a32..1642ec59 100644
--- a/man/model-method-pathfinder.Rd
+++ b/man/model-method-pathfinder.Rd
@@ -150,8 +150,9 @@ pathfinder should return. The number of draws PSIS sampling samples from
 will be equal to \code{single_path_draws * num_paths}.}
 
 \item{draws}{(positive integer) Number of draws to return after performing
-pareto smooted importance sampling (PSIS). This must be smaller than
-\code{single_path_draws * num_paths}.}
+pareto smooted importance sampling (PSIS). This should be smaller than
+\code{single_path_draws * num_paths} (future versions of CmdStan will throw a
+warning).}
 
 \item{num_paths}{(positive integer) Number of single pathfinders to run.}
 

From 9a18fa49327bba59d58e190c07e87aadcf4ee62a Mon Sep 17 00:00:00 2001
From: jgabry 
Date: Wed, 13 Dec 2023 09:18:35 -0700
Subject: [PATCH 16/19] bump version

---
 DESCRIPTION | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index 19d35480..c136bdc0 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: cmdstanr
 Title: R Interface to 'CmdStan'
 Version: 0.7.0
-Date: 2023-11-08
+Date: 2023-12-13
 Authors@R:
     c(person(given = "Jonah", family = "Gabry", role = c("aut", "cre"),
            email = "jsg2201@columbia.edu"),

From 3aa37ac0750b089dbd98e08aa074ed9b225b19a8 Mon Sep 17 00:00:00 2001
From: jgabry 
Date: Wed, 13 Dec 2023 09:38:18 -0700
Subject: [PATCH 17/19] update broken URLs

---
 R/model.R                        | 10 +++++-----
 README.md                        |  2 +-
 man/model-method-check_syntax.Rd |  2 +-
 man/model-method-compile.Rd      |  2 +-
 man/model-method-sample_mpi.Rd   |  6 +++---
 vignettes/cmdstanr-internals.Rmd |  2 +-
 6 files changed, 12 insertions(+), 12 deletions(-)

diff --git a/R/model.R b/R/model.R
index 0ea2132d..0825ef91 100644
--- a/R/model.R
+++ b/R/model.R
@@ -381,7 +381,7 @@ CmdStanModel <- R6::R6Class(
 #' @param pedantic (logical) Should pedantic mode be turned on? The default is
 #'   `FALSE`. Pedantic mode attempts to warn you about potential issues in your
 #'   Stan program beyond syntax errors. For details see the [*Pedantic mode*
-#'   chapter](https://mc-stan.org/docs/reference-manual/pedantic-mode.html) in
+#'   chapter](https://mc-stan.org/docs/stan-users-guide/pedantic-mode.html) in
 #'   the Stan Reference Manual. **Note:** to do a pedantic check for a model
 #'   without compiling it or for a model that is already compiled the
 #'   [`$check_syntax()`][model-method-check_syntax] method can be used instead.
@@ -797,7 +797,7 @@ CmdStanModel$set("public", name = "variables", value = variables)
 #' @param pedantic (logical) Should pedantic mode be turned on? The default is
 #'   `FALSE`. Pedantic mode attempts to warn you about potential issues in your
 #'   Stan program beyond syntax errors. For details see the [*Pedantic mode*
-#'   chapter](https://mc-stan.org/docs/reference-manual/pedantic-mode.html) in
+#'   chapter](https://mc-stan.org/docs/stan-users-guide/pedantic-mode.html) in
 #'   the Stan Reference Manual.
 #' @param include_paths (character vector) Paths to directories where Stan
 #'   should look for files specified in `#include` directives in the Stan
@@ -1273,7 +1273,7 @@ CmdStanModel$set("public", name = "sample", value = sample)
 #'
 #' @description The `$sample_mpi()` method of a [`CmdStanModel`] object is
 #'   identical to the `$sample()` method but with support for
-#'   [MPI](https://mc-stan.org/math/mpi.html). The target audience for MPI are
+#'   MPI (message passing interface). The target audience for MPI are
 #'   those with large computer clusters. For other users, the
 #'   [`$sample()`][model-method-sample] method provides both parallelization of
 #'   chains and threading support for within-chain parallelization.
@@ -1313,8 +1313,8 @@ CmdStanModel$set("public", name = "sample", value = sample)
 #' @return A [`CmdStanMCMC`] object.
 #'
 #' @template seealso-docs
-#' @seealso The Stan Math Library's MPI documentation
-#'   ([mc-stan.org/math/mpi](https://mc-stan.org/math/mpi.html)) for more
+#' @seealso The Stan Math Library's documentation
+#'   ([mc-stan.org/math](https://mc-stan.org/math/)) for more
 #'   details on MPI support in Stan.
 #'
 #' @examples
diff --git a/README.md b/README.md
index 159d0d08..1181b098 100644
--- a/README.md
+++ b/README.md
@@ -70,5 +70,5 @@ open a new one if none are applicable.
 CmdStanR, like CmdStan and the core Stan C++ code, is licensed under the
 following licenses:
 
-- Code: BSD 3-clause (https://opensource.org/licenses/BSD-3-Clause)
+- Code: BSD 3-clause (https://opensource.org/license/bsd-3-clause/)
 - Documentation: CC-BY 4.0 (https://creativecommons.org/licenses/by/4.0/)
diff --git a/man/model-method-check_syntax.Rd b/man/model-method-check_syntax.Rd
index ab1fa8c3..a646a5e1 100644
--- a/man/model-method-check_syntax.Rd
+++ b/man/model-method-check_syntax.Rd
@@ -15,7 +15,7 @@ check_syntax(
 \arguments{
 \item{pedantic}{(logical) Should pedantic mode be turned on? The default is
 \code{FALSE}. Pedantic mode attempts to warn you about potential issues in your
-Stan program beyond syntax errors. For details see the \href{https://mc-stan.org/docs/reference-manual/pedantic-mode.html}{\emph{Pedantic mode} chapter} in
+Stan program beyond syntax errors. For details see the \href{https://mc-stan.org/docs/stan-users-guide/pedantic-mode.html}{\emph{Pedantic mode} chapter} in
 the Stan Reference Manual.}
 
 \item{include_paths}{(character vector) Paths to directories where Stan
diff --git a/man/model-method-compile.Rd b/man/model-method-compile.Rd
index 52578854..d295eedc 100644
--- a/man/model-method-compile.Rd
+++ b/man/model-method-compile.Rd
@@ -33,7 +33,7 @@ same location as the Stan program.}
 
 \item{pedantic}{(logical) Should pedantic mode be turned on? The default is
 \code{FALSE}. Pedantic mode attempts to warn you about potential issues in your
-Stan program beyond syntax errors. For details see the \href{https://mc-stan.org/docs/reference-manual/pedantic-mode.html}{\emph{Pedantic mode} chapter} in
+Stan program beyond syntax errors. For details see the \href{https://mc-stan.org/docs/stan-users-guide/pedantic-mode.html}{\emph{Pedantic mode} chapter} in
 the Stan Reference Manual. \strong{Note:} to do a pedantic check for a model
 without compiling it or for a model that is already compiled the
 \code{\link[=model-method-check_syntax]{$check_syntax()}} method can be used instead.}
diff --git a/man/model-method-sample_mpi.Rd b/man/model-method-sample_mpi.Rd
index 742420d0..8d961e62 100644
--- a/man/model-method-sample_mpi.Rd
+++ b/man/model-method-sample_mpi.Rd
@@ -253,7 +253,7 @@ A \code{\link{CmdStanMCMC}} object.
 \description{
 The \verb{$sample_mpi()} method of a \code{\link{CmdStanModel}} object is
 identical to the \verb{$sample()} method but with support for
-\href{https://mc-stan.org/math/mpi.html}{MPI}. The target audience for MPI are
+MPI (message passing interface). The target audience for MPI are
 those with large computer clusters. For other users, the
 \code{\link[=model-method-sample]{$sample()}} method provides both parallelization of
 chains and threading support for within-chain parallelization.
@@ -303,8 +303,8 @@ The Stan and CmdStan documentation:
 \item CmdStan User’s Guide: \href{https://mc-stan.org/docs/cmdstan-guide/}{mc-stan.org/docs/cmdstan-guide}
 }
 
-The Stan Math Library's MPI documentation
-(\href{https://mc-stan.org/math/mpi.html}{mc-stan.org/math/mpi}) for more
+The Stan Math Library's documentation
+(\href{https://mc-stan.org/math/}{mc-stan.org/math}) for more
 details on MPI support in Stan.
 
 Other CmdStanModel methods: 
diff --git a/vignettes/cmdstanr-internals.Rmd b/vignettes/cmdstanr-internals.Rmd
index cefed0b0..96ef06ea 100644
--- a/vignettes/cmdstanr-internals.Rmd
+++ b/vignettes/cmdstanr-internals.Rmd
@@ -114,7 +114,7 @@ value.
 bounds.
 
 For the latest information on the checks performed in pedantic mode see the
-[Pedantic mode chapter](https://mc-stan.org/docs/reference-manual/pedantic-mode.html)
+[Pedantic mode chapter](https://mc-stan.org/docs/stan-users-guide/pedantic-mode.html)
 in the Stan Reference Manual.
 
 Pedantic mode is available when compiling the model or when using the separate

From b9f27280ac41ab909819c6e3c757f24bbe7e9e5f Mon Sep 17 00:00:00 2001
From: jgabry 
Date: Wed, 13 Dec 2023 09:57:08 -0700
Subject: [PATCH 18/19] try to fix a few R cmd check notes

---
 DESCRIPTION          | 1 +
 R/model.R            | 2 +-
 README.md            | 2 +-
 man/cmdstan_model.Rd | 2 +-
 4 files changed, 4 insertions(+), 3 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index c136bdc0..35154f28 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -45,6 +45,7 @@ Imports:
     rlang (>= 0.4.7)
 Suggests:
     bayesplot,
+    ggplot2,
     knitr (>= 1.37),
     loo (>= 2.0.0),
     rmarkdown,
diff --git a/R/model.R b/R/model.R
index 0825ef91..f55c23be 100644
--- a/R/model.R
+++ b/R/model.R
@@ -1,6 +1,6 @@
 #' Create a new CmdStanModel object
 #'
-#' @description \if{html}{\figure{logo.png}{options: width="25px"}}
+#' @description \if{html}{\figure{logo.png}{options: width=25}}
 #'   Create a new [`CmdStanModel`] object from a file containing a Stan program
 #'   or from an existing Stan executable. The [`CmdStanModel`] object stores the
 #'   path to a Stan program and compiled executable (once created), and provides
diff --git a/README.md b/README.md
index 1181b098..989c2830 100644
--- a/README.md
+++ b/README.md
@@ -27,7 +27,7 @@ releases.
 * Modularity: CmdStanR runs Stan's algorithms and lets downstream modules do the
 analysis.
 
-* Flexible [BSD-3 license](https://opensource.org/licenses/BSD-3-Clause).
+* Flexible [BSD-3 license](https://opensource.org/license/bsd-3-clause/).
 
 
 ### Installation
diff --git a/man/cmdstan_model.Rd b/man/cmdstan_model.Rd
index 26af0c1d..b0432abc 100644
--- a/man/cmdstan_model.Rd
+++ b/man/cmdstan_model.Rd
@@ -31,7 +31,7 @@ more. See \code{\link[=model-method-compile]{$compile()}} for details.}
 A \code{\link{CmdStanModel}} object.
 }
 \description{
-\if{html}{\figure{logo.png}{options: width="25px"}}
+\if{html}{\figure{logo.png}{options: width=25}}
 Create a new \code{\link{CmdStanModel}} object from a file containing a Stan program
 or from an existing Stan executable. The \code{\link{CmdStanModel}} object stores the
 path to a Stan program and compiled executable (once created), and provides

From 9769e8c1b20e6b22be1b1bea37ce83f4a90c3823 Mon Sep 17 00:00:00 2001
From: jgabry 
Date: Wed, 13 Dec 2023 11:03:06 -0700
Subject: [PATCH 19/19] fix failing test

---
 R/fit.R                             | 2 +-
 man/fit-method-hessian.Rd           | 2 +-
 tests/testthat/test-model-methods.R | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/R/fit.R b/R/fit.R
index b16b260f..b1c99e97 100644
--- a/R/fit.R
+++ b/R/fit.R
@@ -457,7 +457,7 @@ CmdStanFit$set("public", name = "grad_log_prob", value = grad_log_prob)
 #'   [unconstrain_variables()], [unconstrain_draws()], [variable_skeleton()],
 #'   [hessian()]
 #'
-hessian <- function(unconstrained_variables, jacobian = TRUE, jacobian_adjustment = TRUE) {
+hessian <- function(unconstrained_variables, jacobian = TRUE, jacobian_adjustment = NULL) {
   if (!is.null(jacobian_adjustment)) {
     warning("'jacobian_adjustment' is deprecated. Please use 'jacobian' instead.", call. = FALSE)
     jacobian <- jacobian_adjustment
diff --git a/man/fit-method-hessian.Rd b/man/fit-method-hessian.Rd
index 9dd54830..8076a889 100644
--- a/man/fit-method-hessian.Rd
+++ b/man/fit-method-hessian.Rd
@@ -6,7 +6,7 @@
 \title{Calculate the log-probability , the gradient w.r.t. each input, and the hessian
 for a given vector of unconstrained parameters}
 \usage{
-hessian(unconstrained_variables, jacobian = TRUE, jacobian_adjustment = TRUE)
+hessian(unconstrained_variables, jacobian = TRUE, jacobian_adjustment = NULL)
 }
 \arguments{
 \item{unconstrained_variables}{(numeric) A vector of unconstrained parameters.}
diff --git a/tests/testthat/test-model-methods.R b/tests/testthat/test-model-methods.R
index c7879288..c3607f7b 100644
--- a/tests/testthat/test-model-methods.R
+++ b/tests/testthat/test-model-methods.R
@@ -3,7 +3,7 @@ context("model-methods")
 set_cmdstan_path()
 mod <- cmdstan_model(testing_stan_file("bernoulli_log_lik"), force_recompile = TRUE)
 data_list <- testing_data("bernoulli")
-fit <- mod$sample(data = data_list, chains = 1)
+fit <- mod$sample(data = data_list, chains = 1, refresh = 0)
 
 test_that("Methods error if not compiled", {
   skip_if(os_is_wsl())