Cran patch (#6)

* cran patch * changelog * new v * cran comments * cran response * cran * cran * edits * more cran changes * commit msg * man changes * cran submit again * new push * add project * remove rm(list=ls()) * remove release tag to rerelease * comments * version # * submitted to CRAN
SensitiveQuestions · Jan 16, 2024 · 96931b7 · 96931b7
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diff --git a/.Rbuildignore b/.Rbuildignore
@@ -6,3 +6,6 @@ README.md
 ^docs$
 _pkgdown.yml
 ^_pkgdown\.yml$
+^CRAN-RELEASE$
+^cran-comments\.md$
+^CRAN-SUBMISSION$
diff --git a/.gitignore b/.gitignore
@@ -13,8 +13,6 @@ vignettes/*.html
 vignettes/*.pdf
 .Rproj.user
 
-*.Rproj
-
 # compiled files 
 src/*.o
 src/*.so
diff --git a/CRAN-SUBMISSION b/CRAN-SUBMISSION
@@ -0,0 +1,3 @@
+Version: 1.4.1
+Date: 2022-05-24 02:06:53 UTC
+SHA: 3b92e9e126af50a898da86d33da08f7354e9682a
diff --git a/ChangeLog b/ChangeLog
@@ -4,4 +4,5 @@ version date      description
 1.2     03/21/15  Added Bayesian implementation for forced response design, including mixed effects option.
 1.3     03/26/15  Bug fixes
 1.3.1   05/13/16  Compatibility with package dependencies
-1.4     08/15/16  Added functionality for including auxiliary information by generalized method of moments
+1.4     08/15/16  Added functionality for including auxiliary information by generalized method of moments
+1.4.1   05/23/22  Changes to stay up to date on CRAN
diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,11 +1,10 @@
 Package: rr
-Version: 1.5
-Date: 2017-8-11
+Version: 1.4.1
 Title: Statistical Methods for the Randomized Response Technique
-Authors@R: c(person("Graeme", "Blair", email = "graeme.blair@ucla.edu", role = c("aut", "cre")),
-             person("Yang-Yang", "Zhou", email = "[email protected]", role = c("aut")),
-             person("Kosuke", "Imai", email = "kimai@princeton.edu", role = c("aut")),
-             person("Winston", "Chou", email = "[email protected]", role = c("ctb")))
+Authors@R: c(person("Graeme", "Blair", email = "graeme.blair@gmail.com", role = c("aut", "cre")),
+             person("Yang-Yang", "Zhou", email = "[email protected]", role = c("aut")),
+             person("Kosuke", "Imai", email = "imai@harvard.edu", role = c("aut")),
+             person("Winston", "Chou", email = "[email protected]", role = c("ctb")))
 Depends:
     R (>= 3.0.0),
     utils
@@ -23,10 +22,11 @@ Description: Enables researchers to conduct multivariate statistical analyses
     are included. The package implements methods described in Blair, Imai, and Zhou
     (2015) ''Design and Analysis of the Randomized Response Technique,'' Journal
     of the American Statistical Association 
-    <http://graemeblair.com/papers/randresp.pdf>.
+    <https://graemeblair.com/papers/randresp.pdf>.
 LazyLoad: yes
 LazyData: yes
 License: GPL (>= 3)
 Suggests:
     testthat
-RoxygenNote: 6.0.1
+Encoding: UTF-8
+RoxygenNote: 7.1.2
diff --git a/NAMESPACE b/NAMESPACE
@@ -6,6 +6,7 @@ S3method(coef,rrreg.bayes)
 S3method(coef,rrreg.bayes.list)
 S3method(coef,rrreg.predictor)
 S3method(predict,rrreg)
+S3method(predict,rrreg.predictor)
 S3method(print,rrreg)
 S3method(print,rrreg.bayes)
 S3method(print,rrreg.predictor)

diff --git a/R/onAttach.R b/R/onAttach.R
diff --git a/R/power.R b/R/power.R
@@ -78,7 +78,7 @@
 #' @references Blair, Graeme, Kosuke Imai and Yang-Yang Zhou. (2015) "Design
 #' and Analysis of the Randomized Response Technique."  \emph{Journal of the 
 #' American Statistical Association.}
-#' Available at \url{http://graemeblair.com/papers/randresp.pdf}.
+#' Available at \url{https://graemeblair.com/papers/randresp.pdf}.
 #' @keywords power analysis
 #' @examples
 #' 
@@ -92,28 +92,6 @@
 #'              design = "forced-known", sig.level = .01,
 #'              type = "one.sample", alternative = "one.sided")
 #' 				       
-#' \dontrun{
-#' 
-#' ## Find power varying the number of respondents from 250 to 2500 and 
-#' ## the population proportion of respondents possessing the sensitive 
-#' ## trait from 0 to .15
-#' 
-#' presp.seq <- seq(from = 0, to = .15, by = .0025)
-#' n.seq <- c(250, 500, 1000, 2000, 2500)
-#' power <- list()
-#' for(n in n.seq) {
-#'     power[[n]] <- rep(NA, length(presp.seq))
-#'     for(i in 1:length(presp.seq))
-#'         power[[n]][i] <- power.rr.test(p = 2/3, p1 = 1/6, p0 = 1/6, n = n, 
-#'                                        presp = presp.seq[i], presp.null = 0,
-#'                                        design = "forced-known", sig.level = .01, 
-#'                                        type = "one.sample",
-#'                                        alternative = "one.sided")$power
-#'     }
-#'     
-#' ## Replicates the results for Figure 2 in Blair, Imai, and Zhou (2014)
-#' }
-#' 
 #' @export
 power.rr.test <- function(p, p0, p1, q, design, n = NULL, r, presp, presp.null = NULL, sig.level, 
                           prespT, prespC, prespT.null = NULL, prespC.null,
@@ -576,6 +554,9 @@ se.f.rr <- function(p, p0, p1, q, design, n, r, presp){
 #' and Analysis of the Randomized Response Technique."  \emph{Working Paper.}
 #' Available at \url{http://imai.princeton.edu/research/randresp.html}.
 #' @keywords power analysis
+#' 
+#' @return Power curve plot
+#' 
 #' @examples
 #' 
 #' ## Generate a power plot for the forced design with known 
@@ -584,7 +565,6 @@ se.f.rr <- function(p, p0, p1, q, design, n, r, presp){
 #' ## 250 to 2500 and the population proportion of respondents 
 #' ## possessing the sensitive trait from 0 to .15.
 #' 
-#' 
 #' presp.seq <- seq(from = 0, to = .15, by = .0025)
 #' n.seq <- c(250, 500, 1000, 2000, 2500)
 #' power.rr.plot(p = 2/3, p1 = 1/6, p0 = 1/6, n.seq = n.seq, 
@@ -655,8 +635,11 @@ power.rr.plot <- function(p, p0, p1, q, design, n.seq, r, presp.seq, presp.null
   }
   }
 
-  if(par == TRUE)
-  par(oma = c(0, 0, 0, 0), mar = c(3.6, 3.6, 0, 0), las = 1, mgp = c(2, .7, 0), tck = -.01, cex = 0.8)  
+  if(par == TRUE) {
+    oldpar <- par(no.readonly = TRUE)
+    on.exit(par(oldpar))
+    par(oma = c(0, 0, 0, 0), mar = c(3.6, 3.6, 0, 0), las = 1, mgp = c(2, .7, 0), tck = -.01, cex = 0.8)  
+  }
 
   plot(0, 1, type = "n", xlim = c(0, max(presp.seq)), ylim = c(0, 1.05), axes = F, 
        xlab = "", ylab = "")

diff --git a/R/rr-package.R b/R/rr-package.R
@@ -25,19 +25,19 @@
 #' @aliases rr-package rr
 #' @docType package
 #' @author Graeme Blair, Experiments in Governance and Politics, Columbia
-#' University \email{graeme.blair@@columbia.edu}, \url{http://graemeblair.com}
+#' University \email{graeme.blair@@gmail.com}, \url{https://graemeblair.com}
 #' 
-#' Kosuke Imai, Department of Politics, Princeton University
-#' \email{kimai@@princeton.edu}, \url{http://imai.princeton.edu}
+#' Kosuke Imai, Departments of Government and Statistics, Harvard University
+#' \email{kimai@@harvard.edu}, \url{https://imai.fas.harvard.edu}
 #' 
-#' Yang-Yang Zhou, Department of Politics, Princeton University
-#' \email{yz3@@princeton.edu}, \url{http://yangyangzhou.com}
+#' Yang-Yang Zhou, Department of Political Science, University of British Columbia
+#' \email{yangyang.zhou@@ubc.ca}, \url{https://www.yangyangzhou.com}
 #' 
-#' Maintainer: Graeme Blair <graeme.blair@@columbia.edu>
+#' Maintainer: Graeme Blair <graeme.blair@@gmail.com>
 #' @references Blair, Graeme, Kosuke Imai and Yang-Yang Zhou. (2015) "Design
 #' and Analysis of the Randomized Response Technique."  
 #' \emph{Journal of the American Statistical Association.}
-#' Available at \url{http://graemeblair.com/papers/randresp.pdf}.
+#' Available at \url{https://graemeblair.com/papers/randresp.pdf}.
 #' @keywords package
 #' @importFrom graphics abline axis lines mtext plot
 #' @importFrom stats as.formula binomial coef complete.cases cov dcauchy df glm glm.control model.frame model.matrix model.matrix.default model.response na.omit pnorm qnorm quantile rnorm runif sd vcov

diff --git a/R/rr.R b/R/rr.R
@@ -586,6 +586,7 @@ coef.rrreg <- function(object, ...){
 #' @param keep.draws Option to return the Monte Carlos draws of the quantity of
 #' interest, for use in calculating differences for example.
 #' @param ... Further arguments to be passed to \code{predict.rrreg()} command.
+#' 
 #' @return \code{predict.rrreg} returns predicted probabilities either for each
 #' observation in the data frame or the average over all observations. The
 #' output is a list that contains the following components:
@@ -611,7 +612,6 @@ coef.rrreg <- function(object, ...){
 #' @keywords predicted probabilities fitted values
 #' @examples
 #' 
-#' \dontrun{
 #' data(nigeria)
 #' 
 #' set.seed(1)
@@ -637,7 +637,6 @@ coef.rrreg <- function(object, ...){
 #'                                 n.sims = 10000)
 #' 
 #' ## Replicates Table 3 in Blair, Imai, and Zhou (2014)
-#' }
 #' 
 #' @importFrom MASS mvrnorm
 #' 

diff --git a/R/rrBayes.R b/R/rrBayes.R
@@ -119,12 +119,13 @@ logistic <- function(x) exp(x)/(1+exp(x))
 #'                          data = nigeria, 
 #'                       p = p, p1 = p1, p0 = p0,
 #'                       design = "forced-known")
+#'                       
+#' \donttest{
 #' 
 #' library(MASS)
 #' draws <- mvrnorm(n = 3, mu = coef(mle.estimates), 
 #'   Sigma = vcov(mle.estimates) * 9)
 #' 
-#' \dontrun{ 
 #' ## run three chains
 #' bayes.1 <- rrreg.bayes(rr.q1 ~ cov.asset.index + cov.married + 
 #'                          I(cov.age/10) + I((cov.age/10)^2) + cov.education + cov.female,   
@@ -146,6 +147,7 @@ logistic <- function(x) exp(x)/(1+exp(x))
 #' bayes <- as.list(bayes.1, bayes.2, bayes.3)
 #' 
 #' summary(bayes)
+#' 
 #' }
 #' 
 #' @importFrom coda mcmc
@@ -211,7 +213,7 @@ rrreg.bayes <- function(formula, p, p0, p1, design, data,
     if(!(group.mixed %in% colnames(data)))
       stop("The covariate named in group.mixed cannot be found in the provided data frame.")
     grp <- data[na.cond == TRUE, paste(group.mixed)]
-    if(class(grp) == "character")
+    if(inherits(grp, "character"))
       grp <- as.factor(grp)
 
     grp.labels <- sort(unique(grp))
@@ -239,11 +241,11 @@ rrreg.bayes <- function(formula, p, p0, p1, design, data,
 
   if (missing("beta.tune")) {
     stop("The Metropolis tuning input object beta.tune is required.")
-  } else if (class(beta.tune) == "numeric" & length(beta.tune) == 1 & nPar > 1) {
+  } else if (inherits(beta.tune, "numeric") & length(beta.tune) == 1 & nPar > 1) {
     beta.tune <- diag(nPar) * beta.tune
-  } else if (class(beta.tune) == "numeric" & length(beta.tune) == nPar) {
+  } else if (inherits(beta.tune, "numeric") & length(beta.tune) == nPar) {
     beta.tune <- diag(beta.tune)
-  } else if (class(beta.tune) == "numeric" | (class(beta.tune) == "matrix" & 
+  } else if (inherits(beta.tune, "numeric") | (inherits(beta.tune, "matrix") & 
                                                 (ncol(beta.tune) != nPar | nrow(beta.tune) != nPar))) {
     stop("The input beta.tune is malformed. It should be a scalar, a vector of length the number of predictors, or a diagonal matrix of dimensions the number of predictors.")
   }
@@ -252,10 +254,10 @@ rrreg.bayes <- function(formula, p, p0, p1, design, data,
 
     if (missing("Psi.start")) {
       Psi.start <- diag(10, ncol(z)) 
-    } else if (class(Psi.start) == "numeric" & 
+    } else if (inherits(Psi.start, "numeric") & 
                  (length(Psi.start) == 1 | length(Psi.start) == ncol(z))) {
       Psi.start <- diag(ncol(z)) * Psi.start
-    } else if (class(Psi.start) == "numeric" | (class(Psi.start) == "matrix" & 
+    } else if (inherits(Psi.start, "numeric") | (inherits(Psi.start, "matrix") & 
                                                   (nrow(Psi.start) != ncol(z) | ncol(Psi.start) != ncol(z)))) {
       stop("The input Psi.start is malformed. It should either be a square matrix of dimensions the number of columns of Z, a scalar, or a vector of length number of columns of Z.")
     }
@@ -265,19 +267,19 @@ rrreg.bayes <- function(formula, p, p0, p1, design, data,
     }
     if (missing("Psi.scale")) {
       Psi.scale <- diag(2, ncol(z)) 
-    } else if (class(Psi.scale) == "numeric" & 
+    } else if (inherits(Psi.scale, "numeric") & 
                  (length(Psi.scale) == 1 | length(Psi.scale) == ncol(z))) {
       Psi.scale <- diag(ncol(z)) * Psi.scale
-    } else if (class(Psi.scale) == "numeric" | (class(Psi.scale) == "matrix" & 
+    } else if (inherits(Psi.scale, "numeric") | (inherits(Psi.scale, "matrix") & 
                  (nrow(Psi.scale) != ncol(z) | ncol(Psi.scale) != ncol(z)))) {
       stop("The input Psi.scale is malformed. It should either be a square matrix of dimensions the number of columns of Z, a scalar, or a vector of length number of columns of Z.")
     }
 
     if (missing("Psi.tune")) {
       stop("The Metropolis tuning input object psi.tune is required.")
-    } else if (class(Psi.tune) == "numeric" & length(Psi.tune) == 1 & length(unique(grp)) > 1) {
+    } else if (inherits(Psi.tune, "numeric") & length(Psi.tune) == 1 & length(unique(grp)) > 1) {
       Psi.tune <- rep(Psi.tune, length(unique(grp)))
-    } else if (class(Psi.tune) == "matrix" | (class(Psi.tune) == "numeric" & (length(Psi.tune) != length(unique(grp))))) {
+    } else if (inherits(Psi.tune, "matrix") | (inherits(Psi.tune, "numeric") & (length(Psi.tune) != length(unique(grp))))) {
       stop("The input Psi.tune is malformed. It should be either a scalar or a vector of length the number of groups.")
     }
   }

diff --git a/R/rrreg.predictor.R b/R/rrreg.predictor.R
@@ -118,7 +118,6 @@ logistic <- function(x) exp(x)/(1+exp(x))
 #' 
 #' ## Fit joint model of responses to an outcome regression of joining a civic 
 #' ## group and the randomized response item of having a militant social connection
-#' \dontrun{
 #' rr.q1.pred.obj <- 
 #'     rrreg.predictor(civic ~ cov.asset.index + cov.married + I(cov.age/10) + 
 #'               I((cov.age/10)^2) + cov.education + cov.female 
@@ -127,7 +126,7 @@ logistic <- function(x) exp(x)/(1+exp(x))
 #'               p = p, p1 = p1, p0 = p0, design = "forced-known")
 #' 
 #' summary(rr.q1.pred.obj)
-#' }
+#'
 #' ## Replicates Table 4 in Blair, Imai, and Zhou (2014)
 #' 
 #' @importFrom arm bayesglm
@@ -518,7 +517,7 @@ print.summary.rrreg.predictor <- function(x, ...){
 #' unrelated question. The design, already specified in the "rrreg.predictor"
 #' object, is then directly inputted into this function.
 #' 
-#' @usage predict.rrreg.predictor(object, fix.z = NULL, alpha = .05,
+#' @usage \method{predict}{rrreg.predictor}(object, fix.z = NULL, alpha = .05,
 #' n.sims = 1000, avg = FALSE, newdata = NULL, quasi.bayes = FALSE, keep.draws
 #' = FALSE, ...)
 #' @param object An object of class "rrreg.predictor" generated by the
@@ -571,11 +570,11 @@ print.summary.rrreg.predictor <- function(x, ...){
 #' @keywords predicted probabilities fitted values
 #' @examples
 #' 
-#' \dontrun{
 #' data(nigeria)
 #' 
 #' ## Define design parameters
 #' 
+#' \donttest{
 #' set.seed(44)
 #' 
 #' p <- 2/3  # probability of answering honestly in Forced Response Design
@@ -597,11 +596,11 @@ print.summary.rrreg.predictor <- function(x, ...){
 #' 
 #' rr.q1.rrreg.predictor.pred <- predict(rr.q1.pred.obj, 
 #'                                  avg = TRUE, quasi.bayes = TRUE, 
-#'                                  n.sims = 10000)
-#' 
+#'                                  n.sims = 1000)
 #' }
 #' @importFrom MASS mvrnorm
 #' @method predict rrreg.predictor
+#' @export
 predict.rrreg.predictor <- function(object, fix.z = NULL, alpha = .05, n.sims = 1000, 
                            avg = FALSE, newdata = NULL, quasi.bayes = FALSE, keep.draws = FALSE, ...) {
 

diff --git a/cran-comments.md b/cran-comments.md
@@ -0,0 +1,25 @@
+## Submission
+
+(Resubmitting with minor change to remove rm(list=ls()) per email from Victoria.)
+
+We are submitting this patch to address the USE_FC_LEN_T issue identified in Ripley's April 5 email. We apologize for the delay in submission and realize it resulted in the package getting pulled.
+
+In addition, the email address for the maintainer has been updated and some other minor changes caught by R CMD CHECK.
+
+Thank you!
+
+## Test environments
+* local OS X install, R 3.5.1
+* ubuntu 12.04 (on travis-ci), R 3.5.1
+* win-builder (devel and release)
+
+## R CMD check results
+
+0 errors | 0 warnings | 0 notes
+
+## Reverse dependencies
+
+There are no reverse dependencies.
+
+---
+
diff --git a/inst/CITATION b/inst/CITATION
@@ -1,17 +1,17 @@
-citHeader("To cite list in publications use:")
-
-citEntry(entry = "misc",
-  title        = "{rr}: Statistical Methods for the Randomized Response Technique",
-  author       = personList(as.person("Graeme Blair"), as.person("Yang-Yang Zhou"),
-                   as.person("Kosuke Imai")),
-  howpublished = "Available at The Comprehensive R Archive Network (CRAN)",
-  year         = "2015",
-  url          = "http://CRAN.R-project.org/package=rr",
-
-  textVersion  =
-  paste("Graeme Blair, Yang-Yang Zhou, and Kosuke Imai (2015).",
-        "rr: Statistical Methods for the Randomized Response Technique.",
-        "Available at The Comprehensive R Archive Network (CRAN).",
-        "URL http://CRAN.R-project.org/package=rr.")
-)
-
+citHeader("To cite list in publications use:")
+
+citEntry(entry = "misc",
+  title        = "{rr}: Statistical Methods for the Randomized Response Technique",
+  author       = personList(as.person("Graeme Blair"), as.person("Yang-Yang Zhou"),
+                   as.person("Kosuke Imai")),
+  howpublished = "Available at The Comprehensive R Archive Network (CRAN)",
+  year         = "2015",
+  url          = "https://CRAN.R-project.org/package=rr",
+
+  textVersion  =
+  paste("Graeme Blair, Yang-Yang Zhou, and Kosuke Imai (2015).",
+        "rr: Statistical Methods for the Randomized Response Technique.",
+        "Available at The Comprehensive R Archive Network (CRAN).",
+        "URL https://CRAN.R-project.org/package=rr")
+)
+