fix the cut, I(), and dim to .Dim issues; updated the bibentry; updat…

…ed cran-comments

DESCRIPTION

@@ -1,8 +1,8 @@
 Package: baker
 Type: Package
 Title: Nested Partially Latent Class Models
-Version: 1.0.0.9000
-Date: 2022-02-02
+Version: 1.0.2
+Date: 2023-12-14
 Authors@R: c(
      person("Zhenke", "Wu", email="zhenkewu@gmail.com",role=c("cre","aut","cph"),
      comment = c(ORCID = "0000-0001-7582-669X")),
@@ -23,7 +23,7 @@ Description: Provides functions to specify, fit and visualize
     of interest sum to one hundred percent, the PERCH scientists frequently refer to
     them as "population etiology pie" and "individual etiology pie", hence the name of the package.
 Depends:
-    R(>= 3.5.0)
+    R(>= 4.3.0)
 Imports:
     rjags(>= 4-6),
     R2jags(>= 0.5),
@@ -44,7 +44,7 @@ Imports:
     abind
 License: MIT + file LICENSE
 Language: en-US
-SystemRequirements: JAGS (>= 4.1.0) (http://mcmc-jags.sourceforge.net)
+SystemRequirements: JAGS (>= 4.3.2) (http://mcmc-jags.sourceforge.net)
 Suggests:
     spelling,
     knitr,
@@ -53,7 +53,7 @@ Suggests:
     covr,
     knitcitations
 VignetteBuilder: knitr
-RoxygenNote: 7.1.1
+RoxygenNote: 7.2.3
 Encoding: UTF-8
 URL: https://github.com/zhenkewu/baker
 BugReports: https://github.com/zhenkewu/baker/issues

R/example_data.R

@@ -68,7 +68,7 @@
 #' @usage data("data_nplcm_reg_nest")
 "data_nplcm_reg_nest"
 
-#' Simulated dataset that is structured in the format necessary for an [nplcm()] with regression 
+#' Simulated dataset that is structured in the format necessary for an [nplcm()] without regression 
 #'
 #' Data set for illustrating regression functionalities
 #' 
@@ -86,7 +86,6 @@
 "data_nplcm_noreg"
 
 
-
 # NB: a few more data sets: data_nplcm_no_reg (no regression setting); this can be used in a lot of functions
 # for visualization. Perhaps also need to add posterior samples as data set just as a way to 
 # demonstrate the plotting functionality and unit test.

R/nplcm-read-folder.R

@@ -235,7 +235,7 @@ nplcm_read_folder <- function(DIR_NPLCM){
 #'   n.itermcmc = as.integer(200), 
 #'   n.burnin = as.integer(100), 
 #'   n.thin = 1,
-#'   individual.pred = TRUE, # <- must set to TRUE! <------- NOTE! 
+#'   individual.pred = TRUE, # <- must set to TRUE! 
 #'   ppd = FALSE,
 #'   result.folder = thedir,
 #'   bugsmodel.dir = thedir
@@ -251,9 +251,8 @@ nplcm_read_folder <- function(DIR_NPLCM){
 #' 
 #' rjags::load.module("glm")
 #' 
-#' nplcm_noreg <- nplcm(data_nplcm_noreg,model_options_no_reg,mcmc_options_no_reg)
-#' 
-#' image(get_individual_prediction(nplcm_noreg))
+#' fitted_nplcm_noreg <- nplcm(data_nplcm_noreg,model_options_no_reg,mcmc_options_no_reg)
+#' image(get_individual_prediction(fitted_nplcm_noreg))
 #' 
 #' }
 #' 

R/nplcm.R

@@ -209,7 +209,7 @@ nplcm <- function(data_nplcm,model_options,mcmc_options){
   Mobs <- data_nplcm$Mobs
   Y    <- data_nplcm$Y
   if (length(rle(Y)[["values"]])>2 | Y[1]!=1) {
-  stop("==[baker] 'data_nplcm$Y' must have cases on top of controls. 
+    stop("==[baker] 'data_nplcm$Y' must have cases on top of controls. 
   Use 'baker::subset_data_nplcm_by_index()' to shuffle the rows. Then retry.==\n")}
   X    <- data_nplcm$X
 
@@ -232,7 +232,7 @@ nplcm <- function(data_nplcm,model_options,mcmc_options){
   } 
   if (do_reg & !any(do_nested)){
     if (do_discrete){
-    fitted_type   <- "reg_nonest_strat"
+      fitted_type   <- "reg_nonest_strat"
       # when every regression is a regression upon discrete variables;
       # when it is not a regression, the fitting function treats it as a single stratum
       # when specifying the model in the .bug file (in the assign_model function, ~1 
@@ -543,7 +543,8 @@ nplcm_fit_NoReg<-
 
       for(i in seq_along(JBrS_list)){
         assign(paste("JBrS", i, sep = "_"), JBrS_list[[i]])    
-        assign(paste("MBS", i, sep = "_"), as.matrix_or_vec(MBS_list[[i]])) 
+        xx <- as.matrix_or_vec(MBS_list[[i]]);  attr(xx,"dimnames") <- NULL # remove dimnames.
+        assign(paste("MBS", i, sep = "_"), xx)
         assign(paste("templateBS", i, sep = "_"), as.matrix_or_vec(template_BrS_list[[i]]))   
       }
 
@@ -995,7 +996,13 @@ nplcm_fit_NoReg<-
          file = curr_data_txt_file)
     ## fix dimension problem.... convert say .Dmi=7:6 to c(7,6) (an issue for templateBS_1):
     bad_jagsdata_txt <- readLines(curr_data_txt_file)
-    good_jagsdata_txt <- gsub( "([0-9]+):([0-9]+)", "c(\\1,\\2)", bad_jagsdata_txt,fixed = FALSE)
+    #good_jagsdata_txt <- gsub( "([0-9]+):([0-9]+)", "c(\\1,\\2)", bad_jagsdata_txt,fixed = FALSE)
+    ## to add an additional complicatoin of dump creates a text file with , dim = but the JAGS only accepts .Dim=
+    good_jagsdata_txt <- gsub( ", dim =", ", .Dim=", 
+                               gsub( "([0-9]+):([0-9]+)", "c(\\1,\\2)", bad_jagsdata_txt,fixed = FALSE),
+                               fixed = FALSE)
+
+
     writeLines(good_jagsdata_txt, curr_data_txt_file)
 
     # fix dimension problem.... convert say 7:6 to c(7,6) (an issue for a dumped matrix):
@@ -1007,6 +1014,15 @@ nplcm_fit_NoReg<-
       good_jagsinits_txt <- gsub( "([0-9]+):([0-9]+)", "c(\\1,\\2)", bad_jagsinits_txt,fixed = FALSE)
       writeLines(good_jagsinits_txt, curr_inits_txt_file)
     }
+
+    ## fixed some problems of JAGS 4.3.2 not having cut function; and I(a,b) functions weirdly, even though
+    ## the two elements are already constants (errors says that are not constant).
+    curr_model_txt_file <- file.path(mcmc_options$result.folder,"model_NoReg.bug")
+    bad_model_txt <- readLines(curr_model_txt_file)
+    good_model_txt <- gsub( "cut\\(", "(", bad_model_txt,fixed = FALSE)
+    good_model_txt <- gsub( "I\\(0\\.000001,0\\.999999\\)", " ", good_model_txt,fixed = FALSE)
+    writeLines(good_model_txt, curr_model_txt_file)
+
     if(is.null(mcmc_options$jags.dir)){mcmc_options$jags.dir=""}
     gs <- jags2_baker(data   = curr_data_txt_file,
                       inits  = in_init,
@@ -1237,7 +1253,8 @@ nplcm_fit_Reg_discrete_predictor_NoNest <-
         attributes(Z_FPR_list[[i]])[names(attributes(Z_FPR_list[[i]]))!="dim"] <- NULL
 
         assign(paste("JBrS", i, sep = "_"), JBrS_list[[i]])    
-        assign(paste("MBS", i, sep = "_"), as.matrix_or_vec(MBS_list[[i]])) 
+        xx <- as.matrix_or_vec(MBS_list[[i]]);  attr(xx,"dimnames") <- NULL # remove dimnames.
+        assign(paste("MBS", i, sep = "_"), xx)
         assign(paste("templateBS", i, sep = "_"), as.matrix_or_vec(template_BrS_list[[i]]))  
 
         # Z_FPR0       <- Z_FPR_list[[i]]
@@ -1943,7 +1960,8 @@ nplcm_fit_Reg_NoNest <-
         attributes(Z_FPR_list[[i]])[names(attributes(Z_FPR_list[[i]]))!="dim"] <- NULL
 
         assign(paste("JBrS", i, sep = "_"), JBrS_list[[i]])    
-        assign(paste("MBS", i, sep = "_"), as.matrix_or_vec(MBS_list[[i]])) 
+        xx <- as.matrix_or_vec(MBS_list[[i]]);  attr(xx,"dimnames") <- NULL # remove dimnames.
+        assign(paste("MBS", i, sep = "_"), xx)
         assign(paste("templateBS", i, sep = "_"), as.matrix_or_vec(template_BrS_list[[i]]))  
 
         # Z_FPR0       <- Z_FPR_list[[i]]
@@ -2519,7 +2537,7 @@ nplcm_fit_Reg_Nest <- function(data_nplcm,model_options,mcmc_options){
   }
 
   if(is.null(prior$Eti_hyper_pflex)){
-     prior$Eti_hyper_pflex = c(1,0.5)
+    prior$Eti_hyper_pflex = c(1,0.5)
     # beta prior for selecting constant vs non-constant additive component
   }
 
@@ -2651,7 +2669,8 @@ nplcm_fit_Reg_Nest <- function(data_nplcm,model_options,mcmc_options){
       attributes(Z_FPR_list[[i]])[names(attributes(Z_FPR_list[[i]]))!="dim"] <- NULL
 
       assign(paste("JBrS", i, sep = "_"), JBrS_list[[i]])    
-      assign(paste("MBS", i, sep = "_"), as.matrix_or_vec(MBS_list[[i]])) 
+      xx <- as.matrix_or_vec(MBS_list[[i]]);  attr(xx,"dimnames") <- NULL # remove dimnames.
+      assign(paste("MBS", i, sep = "_"), xx)
       assign(paste("templateBS", i, sep = "_"), as.matrix_or_vec(template_BrS_list[[i]]))  
 
       # Z_FPR0       <- Z_FPR_list[[i]]

R/utils.R

@@ -930,11 +930,11 @@ set_strat <- function(X,X_reg) {
 #'
 #' @details Note that this function should be used with caution. It used
 #' \deqn{nrow(X)/nrow(unique(X[,X_reg,drop=FALSE]))>10} as an *ad hoc* criterion.
-#' It is not the same as `plyr::is.discrete()`
+#' It is not the same as `is.discrete()` in `plyr`
 #'
 #' @param X   A data frame of covariates
 #' @param X_reg The vector of covariates that will stratify the analyses. These
-#' variables have to be categorical. Or a formula (can be tested by `plyr::is.formula`), 
+#' variables have to be categorical. Or a formula (can be tested by `is.formula` in `plyr`), 
 #' e.g., `~as.factor(SITE8) + as.factor(AGECAT > 1)`.
 #'
 #' @return `TRUE` for all being discrete; `FALSE` otherwise.
@@ -1501,6 +1501,7 @@ as.matrix_or_vec <- function(x){
   as.matrix(x)
 }
 
+
 #' get index of latent status
 #' 
 #' @param cause_list see mode_options in [nplcm()]

README.Rmd

@@ -258,7 +258,7 @@ date
 2. Install [JAGS 4.2.0](https://sourceforge.net/projects/mcmc-jags/files/JAGS/4.x/Windows/); Add the path to JAGS 4.2.0 into the environmental variable (essential for R to find the jags program). See [this](https://superuser.com/questions/949560/how-do-i-set-system-environment-variables-in-windows-10) for setting environmental variables;
 - alternatives are `brew install -v jags` for OSX, `sudo apt-get install jags` for Ubuntu/Debian
 3. Fire up `R`, run `R` command `install.packages("rjags")`
-4. Install [`Rtools`](https://cran.r-project.org/bin/windows/Rtools/) (for building and installing R pacakges from source); Add the path to `Rtools` (e.g. `C:\Rtools\`) into your environmental variables so that R knows where to find it. 
+4. Install [`Rtools`](https://cran.r-project.org/bin/windows/Rtools/) (for building and installing R pacakges from source); Add the path to `Rtools` (e.g., `C:\Rtools\`) into your environmental variables so that R knows where to find it. 
 
 <!-- - [WinBUGS 1.4.3](http://www.mrc-bsu.cam.ac.uk/software/bugs/the-bugs-project-winbugs/) -->
 <!--     1. Install the [patch](http://www.mrc-bsu.cam.ac.uk/software/bugs/the-bugs-project-winbugs/the-bugs-project-winbugs-patches/) -->

README.md

@@ -14,7 +14,7 @@ status](https://ci.appveyor.com/api/projects/status/github/zhenkewu/baker?branch
 [![](http://cranlogs.r-pkg.org/badges/grand-total/baker?color=green)](https://cran.r-project.org/package=baker)
 [![](http://cranlogs.r-pkg.org/badges/last-month/baker?color=green)](https://cran.r-project.org/package=baker)
 [![](http://cranlogs.r-pkg.org/badges/last-week/baker?color=green)](https://cran.r-project.org/package=baker)
-[![](https://img.shields.io/badge/devel%20version-1.0.0.9000-blue.svg)](https://github.com/zhenkewu/baker)
+[![](https://img.shields.io/badge/devel%20version-1.0.1-blue.svg)](https://github.com/zhenkewu/baker)
 [![](https://img.shields.io/badge/lifecycle-maturing-blue.svg)](https://lifecycle.r-lib.org/articles/stages.html#maturing)
 
 <!-- [![Coverage status](https://codecov.io/gh/zhenkewu/baker/branch/master/graph/badge.svg)](https://codecov.io/github/zhenkewu/baker?branch=master) -->
@@ -303,8 +303,8 @@ date
 3.  Fire up `R`, run `R` command `install.packages("rjags")`
 4.  Install [`Rtools`](https://cran.r-project.org/bin/windows/Rtools/)
     (for building and installing R pacakges from source); Add the path
-    to `Rtools` (e.g. `C:\Rtools\`) into your environmental variables so
-    that R knows where to find it.
+    to `Rtools` (e.g., `C:\Rtools\`) into your environmental variables
+    so that R knows where to find it.
 
 <!-- - [WinBUGS 1.4.3](http://www.mrc-bsu.cam.ac.uk/software/bugs/the-bugs-project-winbugs/) -->
 <!--     1. Install the [patch](http://www.mrc-bsu.cam.ac.uk/software/bugs/the-bugs-project-winbugs/the-bugs-project-winbugs-patches/) -->

appveyor.yml

@@ -9,8 +9,8 @@ init:
 
 install:
   - ps: Bootstrap
-  - ps: (new-object net.webclient).DownloadFile('https://sourceforge.net/projects/mcmc-jags/files/JAGS/4.x/Windows/JAGS-4.2.0-Rtools33.exe', 'C:\projects\JAGS-4.2.0.exe')
-  - cmd: C:\projects\JAGS-4.2.0.exe /S
+  - ps: (new-object net.webclient).DownloadFile('https://sourceforge.net/projects/mcmc-jags/files/JAGS/4.x/Windows/JAGS-4.3.1.exe', 'C:\projects\JAGS-4.3.1.exe')
+  - cmd: C:\projects\JAGS-4.3.1.exe /S
 
 
 cache:

cran-comments.md

@@ -1,30 +1,38 @@
 ## Resubmission
 This is a resubmission. In this version I have:
 
-* exported functions that used `baker:::`
+* updated to fix an issue in the example of `nplcm` (Package was archived on CRAN because of a tricky interfacing issue between JAGS 4.3.x and R 4.3.x)
+
+* [only appeared on Windows Server 2022, R-devel, 64 bit]checking for detritus in the temp directory ... NOTE
+Found the following files/directories:
+  'lastMiKTeXException'
+  We checked online; it seems to be platform-specific and related to files produced during testing; we did not find any place in the source files where interactive use of the vignette was invoked. This note did not appear in other platforms.
+
+* There is another note (occasionally) about some doi links being "Service Unavailable". 
+  I checked that every link works. They were considered to be slow (by the checking functions) 
+  when directed to the page.
+
+* Previously I was asked to ignore the following note about spelling (for author names)
+  Possibly misspelled words in DESCRIPTION:
+       al (17:49, 19:11)
+       et (17:46, 19:8)
+
+## Reverse dependencies
+
+This is a precise fix to a previously a short and archived release taken down on 2022-06-08 (https://cran.r-project.org/web/packages/baker/index.html), so there are no reverse dependencies
+given the short life of the previous release.
 
-* removed all occurrences of `rm(list = ls())`
 
 
 ## Test environments
-* local OS X install, R 4.0.2
+* local OS X install, R 4.3.1
 * ubuntu latest release (on Github-Action), R 4.1.2
 * win-builder (devel and release), R 4.1.2
 
 ## R CMD check results
 
 0 errors | 0 warnings | 1 note
 
-* New submission
-
-* [only appeared on Windows Server 2022, R-devel, 64 bit]checking for detritus in the temp directory ... NOTE
-Found the following files/directories:
-  'lastMiKTeXException'
-  We checked online; it seems to be platform-specific and related to files produced during testing; we did not find any place in the source files where interactive use of the vignette was invoked. This note did not appear in other platforms.
-
-## Reverse dependencies
-
-This is a new release, so there are no reverse dependencies.
 
 ---
 

inst/CITATION

@@ -1,15 +1,34 @@
-citHeader("To cite baker in publications, please use:")
-
-citEntry(entry = "Article",
-  author = personList(as.person("Zhenke Wu"),
-             as.person("Maria Deloria-Knoll"),
-             as.person("Laura L Hammitt"),
-             as.person("Scott L Zeger")),
+bibentry(
+  bibtype = "Article",
   title = "Partially Latent Class Models for Case-Control Studies
   of Childhood Pneumonia Aetiology",
+  author = c(person("Zhenke", "Wu"),
+  person("Maria",  "Deloria-Knoll"),
+  person("Laura L", "Hammitt"),
+  person("Scott L", "Zeger")
+  ),
   journal = "Journal of the Royal Statistical Society: Series C (Applied Statistics)",
   year = "2015",
-  doi = "10.1111/rssc.12101",
+  volume = "65",
+  number = "1",
+  pages = " 97–114",
   url = "https://rss.onlinelibrary.wiley.com/doi/full/10.1111/rssc.12101",
-  textVersion = "Wu, Z., Deloria-Knoll, M., Hammitt, L.L., Zeger, S.L. and the Pneumonia Etiology Research for Child Health Core Team (2016), Partially latent class models for case-control studies of childhood pneumonia aetiology. Journal of the Royal Statistical Society: Series C (Applied Statistics). doi: 10.1111/rssc.12101."
+  doi = "10.1111/rssc.12101"
+)
+
+
+bibentry(
+  bibtype = "Article",
+  title = "Nested partially latent class models for dependent binary data; estimating disease etiology",
+  author = c(person("Zhenke", "Wu"),
+  person("Maria",  "Deloria-Knoll"),
+  person("Scott L", "Zeger")
+  ),
+  journal = "Biostatistics",
+  year = "2017",
+  volume = "18",
+  number = "2",
+  pages = " 200–213",
+  url = "https://academic.oup.com/biostatistics/article/18/2/200/2555349",
+  doi = "10.1093/biostatistics/kxw037"
 )