adjusted issues

.Rbuildignore

@@ -1,13 +1,11 @@
 ^.*\.Rproj$
 ^\.Rproj\.user$
 ^LICENSE$
-
 docs
 ^\.github$
 ^_pkgdown\.yml$
 ^dev$
 ^doc$
-^Meta$
 ^cran-comments\.md$
 ^cran_submission_script\.R$
 ^CRAN-SUBMISSION$

.gitignore

@@ -2,6 +2,7 @@
 .Rhistory
 .RData
 .Ruserdata
-/doc/
-/Meta/
+Meta
 .DS_Store
+doc
+docs

DESCRIPTION

@@ -28,7 +28,7 @@ Description: Bayesian MCPMod (Fleischer et al. (2022)
     Estimated dose-response relationships can be bootstrapped and
     visualized.
 License: Apache License (>= 2)
-URL: https://github.com/Boehringer-Ingelheim/BayesianMCPMod
+URL: https://boehringer-ingelheim.github.io/BayesianMCPMod/, https://github.com/Boehringer-Ingelheim/BayesianMCPMod
 BugReports: https://github.com/Boehringer-Ingelheim/BayesianMCPMod/issues
 Depends:
     R (>= 4.2)
@@ -40,20 +40,20 @@ Imports:
     RBesT,
     stats
 Suggests:
-    reactable,
-    tibble,
-    quarto,
     clinDR,
-    dplyr,
-    knitr,
-    rmarkdown,
-    MCPModPack,
     data.table,
     doFuture,
+    quarto,
     doRNG,
+    dplyr,
     kableExtra,
+    knitr,
+    MCPModPack,
+    reactable,
+    rmarkdown,
     spelling,
-    testthat (>= 3.0.0)
+    testthat (>= 3.0.0),
+    tibble
 VignetteBuilder: quarto
 Config/testthat/edition: 3
 Encoding: UTF-8

R/posterior.R

@@ -2,7 +2,7 @@
 #'
 #' @description Either the patient level data or both mu_hat as well as S_hat must to be provided.
 #' If patient level data is provided mu_hat and S_hat are calculated within the function using a linear model.
-#' This function calculates the posterior distribution. Depending on the input for S_hat this step is either performed for every dose group independently via the RBesT function postmix() or the mvpostmix() function of the dosefinding package is utilized. 
+#' This function calculates the posterior distribution. Depending on the input for S_hat this step is either performed for every dose group independently via the RBesT function postmix() or the mvpostmix() function of the DoseFinding package is utilized.
 #' In the latter case conjugate posterior mixture of multivariate normals are calculated (DeGroot 1970, Bernardo and Smith 1994)
 #'
 #' @param prior_list a prior list with information about the prior to be used for every dose group

_pkgdown.yml

@@ -14,3 +14,4 @@ articles:
   - analysis_normal
   - Simulation_Example
   - Comparison_vignette
+  - Simulation_Comparison

inst/WORDLIST

@@ -57,3 +57,22 @@ sd
 se
 simulateData
 summand
+AFM
+bayesian
+BMCPMod
+BRINTELLIX
+clinDR
+colours
+dr
+eqn
+getBootstrapSamples
+getPriorList
+Jl
+MADRS
+MCPModPack
+MDD
+mvpostmix
+normals
+powMCT
+DeGroot
+sigEMAX

tests/testthat/setup.R

@@ -29,9 +29,9 @@ getPriorList <- function (
 
   gmap <- RBesT::gMAP(
     formula    = cbind(est, se) ~ 1 | trial,
+    family     = gaussian,
     weights    = hist_data$n,
     data       = hist_data,
-    family     = gaussian,
     beta.prior = cbind(0, 100 * sd_tot),
     tau.dist   = "HalfNormal",
     tau.prior  = cbind(0, sd_tot / 4))

vignettes/Comparison_vignette.qmd

@@ -12,8 +12,8 @@ format:
     warning: false
 vignette: >
   %\VignetteIndexEntry{Simulation Example of Bayesian MCPMod and MCPMod}
-    %\VignetteEncoding{UTF-8}
-    %\VignetteEngine{quarto::html}
+  %\VignetteEngine{quarto::html}
+  %\VignetteEncoding{UTF-8}
 ---
 
 ```{r setup, include=FALSE, eval=TRUE, message=FALSE, warning=FALSE}
@@ -805,7 +805,7 @@ kable(results_monotonic_MCP_nsample)%>%
 
 ### varying expected effect for maximum dose
 
-For the simulations of the non-monotonic scenario, the R package 'Dosefinding' was used instead of 'MCPModPack'. In particular the powMCT function was utilized to calculate success probabilities for the various scenarios.
+For the simulations of the non-monotonic scenario, the R package 'DoseFinding' was used instead of 'MCPModPack'. In particular the powMCT function was utilized to calculate success probabilities for the various scenarios.
 
 ```{r}
 # linear with DoseFinding package
@@ -1585,7 +1585,7 @@ uninf_prior_list <- list(
 
 To calculate success probabilities for the different assumed dose-response models and the specified trial design we will apply the assessDesign function. 
 
-## Minimal scnenario
+## Minimal scenario
 ### varying expected effect for maximum dose
 
 
@@ -2058,7 +2058,7 @@ var_nsample_Bay$kable_result
 
 # Comparison
 
-In the following, the comparisons between the success probabilities (i.e.power values for frequentist set-up) of various scenarios and differnt parameters are visualized.
+In the following, the comparisons between the success probabilities (i.e.power values for frequentist set-up) of various scenarios and different parameters are visualized.
 
 The following plots show the difference between the results from MCPModPack and BayesianMCPMod. The results of MCPModPack are shown as a line and the difference to the result with BayesianMCPMod is presented as a bar. The results for the different assumed true dose-response models (that were the basis for simulating the data) are shown in different colours.
 
@@ -2437,7 +2437,7 @@ ggplot(data = data_plot_nsample_non_monotonic, aes(x = sample_sizes_num)) +
 
 ```
 
-### variability sceanrio
+### variability scenario
 
 ```{r}
 

vignettes/Simulation_Comparison.qmd

@@ -12,8 +12,8 @@ format:
     warning: false
 vignette: >
   %\VignetteIndexEntry{Comparison of Bayesian MCPMod and MCPMod}
-    %\VignetteEncoding{UTF-8}
-    %\VignetteEngine{quarto::html}
+  %\VignetteEngine{quarto::html}
+  %\VignetteEncoding{UTF-8}
 ---
 
 ```{r setup, include=FALSE, eval=TRUE, message=FALSE, warning=FALSE}
@@ -557,7 +557,7 @@ monotonic_Bay <- print_result_Bay_max_eff(results_monotonic_Bay, c(monotonic_sce
 
 # Comparison
 
-In the following, the comparisons between the success probabilities (i.e.power values for frequentist set-up) of various scenarios and differnt parameters are visualized.
+In the following, the comparisons between the success probabilities (i.e.power values for frequentist set-up) of various scenarios and different parameters are visualized.
 
 The following plots show the difference between the results from MCPModPack and BayesianMCPMod. The results of MCPModPack are shown as a line and the difference to the result with BayesianMCPMod is presented as a bar. The results for the different assumed true dose-response models (that were the basis for simulating the data) are shown in different colours.
 

vignettes/Simulation_Example.qmd

@@ -14,8 +14,8 @@ format:
     warning: false
 vignette: >
   %\VignetteIndexEntry{Simulation Example of Bayesian MCPMod for Continuous Data}
-    %\VignetteEncoding{UTF-8}
-    %\VignetteEngine{quarto::html}
+  %\VignetteEngine{quarto::html}
+  %\VignetteEncoding{UTF-8}
 ---
 
 ```{r, include = FALSE}
@@ -103,7 +103,7 @@ prior_list  <- list(
 
 # Specification of new trial design 
 
-For the hypothetical new trial, we plan with 4 active dose levels \eqn{2.5, 5, 10, 20} and we specify a broad set of potential dose-response relationships, including a linear, an exponential, an emax and 2 sigEMax models.  
+For the hypothetical new trial, we plan with 4 active dose levels \eqn{2.5, 5, 10, 20} and we specify a broad set of potential dose-response relationships, including a linear, an exponential, an emax and 2 sigEMAX models.  
 Furthermore, we assume a maximum effect of -3 on top of control (i.e. assuming that active treatment can reduce the MADRS score after 8 weeks by up to 15.8) and plan a trial with 80 patients for all active groups and 60 patients for control.
 ```{r}
 exp     <- DoseFinding::guesst(

vignettes/analysis_normal.qmd

@@ -13,8 +13,8 @@ format:
     warning: false
 vignette: >
   %\VignetteIndexEntry{Analysis Example of Bayesian MCPMod for Continuous Data}
-    %\VignetteEncoding{UTF-8}
-    %\VignetteEngine{quarto::html}
+  %\VignetteEngine{quarto::html}
+  %\VignetteEncoding{UTF-8}
 ---