first submission to CRAN

saudiwin · Feb 10, 2023 · 274dff2 · 274dff2
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diff --git a/.Rbuildignore b/.Rbuildignore
@@ -17,3 +17,4 @@ continuous.png
 ex_code_plot.R
 README.md
 anim_ggplot.gif
+overwrite_datasets.R
diff --git a/DESCRIPTION b/DESCRIPTION
@@ -16,6 +16,7 @@ BugReports: https://github.com/saudiwin/ordbetareg_pack/issues
 License: MIT + file LICENSE
 Encoding: UTF-8
 LazyData: true
+LazyDataCompression: xz
 Roxygen: list(markdown = TRUE)
 RoxygenNote: 7.1.2
 Depends: 

diff --git a/NEWS.md b/NEWS.md
@@ -7,6 +7,8 @@
 - Improved plot formatting and added theme and label options.
 - Updated vignette to include information about `glmmTMB` as an alternative for estimation
 and new plot functions.
+- Added distribution `rordbeta` and `dordbeta` functions.
+- Removed legacy `0 + Intercept` function code. Now accepts any kind of formula.
 
 # ordbetareg v0.5.0
 

diff --git a/R/distribution.R b/R/distribution.R
@@ -21,7 +21,7 @@
 #' @param cutpoints A vector of two numeric values for the cutpoints. Second value should
 #' @param log where to return the log density
 #' be strictly greater than the first value.
-#' @example
+#' @examples
 #'
 #' # examine density (likelihood) of different possible values
 #' # given fixed values for ordered beta parameters

diff --git a/R/modeling.R b/R/modeling.R
@@ -115,6 +115,7 @@
 #' @examples
 #' # load survey data that comes with the package
 #'
+#' library(dplyr)
 #' data("pew")
 #'
 #' # prepare data
@@ -948,6 +949,8 @@ ordbetareg <- function(formula=NULL,
 #'
 #' data("sim_data")
 #'
+#' library(dplyr)
+#'
 #' # will take a while to run this
 #' \donttest{
 #'     sim_data <- sim_ordbeta(N=c(250,750),

diff --git a/cran-comments.md b/cran-comments.md
@@ -2,31 +2,32 @@
 
 0 errors | 0 warnings | 1 notes
 
-> checking installed package size ... NOTE
-    installed size is  5.7Mb
+❯ checking installed package size ... NOTE
+    installed size is 13.4Mb
     sub-directories of 1Mb or more:
-      data   5.2Mb
+      data  10.0Mb
+      doc    3.3Mb
 
 The data sub-directory contains fitted models that are used to illustrate function examples because fitting the model can take up to an hour. 
 
-Submitting to win-release results in OK status - no NOTEs or WARNINGs.
+Submitting to win-release results in 1 ERROR: 
 
-Submitting to win-devel results in the following WARNING:
+Error(s) in re-building vignettes:
+--- re-building 'package_introduction.Rmd' using rmarkdown
+Quitting from lines 247-258 (package_introduction.Rmd) 
+Error: processing vignette 'package_introduction.Rmd' failed with diagnostics:
+could not find function "avg_slopes"
+--- failed re-building 'package_introduction.Rmd'
 
-Found the following significant warnings:
-  Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
-See 'd:/RCompile/CRANguest/R-devel/ordbetareg.Rcheck/00install.out' for details.
+SUMMARY: processing the following file failed:
+  'package_introduction.Rmd'
 
-The ggplot2 3.4.0 package is not currently available in CRAN. To address this issue, I added code that will use the updated function argument if ggplot2 3.4.0 is detected (see plot.R line number 154):
+Error: Vignette re-building failed.
+Execution halted
 
-```
-if(packageVersion('ggplot2')=="3.4.0") {
+Submitting to win-devel results in OK status - no NOTEs or WARNINGs.
 
-  cont_plot <- cont_plot + geom_density(aes(x=true),linewidth=2,colour="gray",alpha=0.7)
+The error in win-release appears to be due to the fact the marginaleffects package, which has the avg_slopes function, does not have a binary available for windows for the current version of the package, 0.9.0. This is the required version listed under the Suggests field in ordbetareg but win-release is installing an older version that does not have the avg_slopes function. However, it is necessary to update to 0.9.0 as otherwise the package vignette fails to compile, and I received an email from the CRAN maintainer requesting that I update the package to handle marginaleffects 0.9.0--see build logs at https://cran.r-project.org/web/checks/check_results_ordbetareg.html. If I do not update the package by 2-16-2023, it will be removed from CRAN.
 
-} else {
+As such, it would seem that this error in win-release should be ignored as it is not clear why it is not installing the current version of marginaleffects, which has been available since Feb 1 2023.
 
-  cont_plot <- cont_plot + geom_density(aes(x=true),size=2,colour="gray",alpha=0.7)
-
-}
-```
diff --git a/data/fit_imputed.rda b/data/fit_imputed.rda
diff --git a/data/fit_multivariate.rda b/data/fit_multivariate.rda
diff --git a/data/ord_fit_mean.rda b/data/ord_fit_mean.rda
diff --git a/data/ord_fit_phi.rda b/data/ord_fit_phi.rda
diff --git a/man/dordbeta.Rd b/man/dordbeta.Rd
diff --git a/man/ordbetareg.Rd b/man/ordbetareg.Rd
diff --git a/man/sim_ordbeta.Rd b/man/sim_ordbeta.Rd
diff --git a/vignettes/package_introduction.Rmd b/vignettes/package_introduction.Rmd
@@ -244,7 +244,6 @@ modelsummary(ord_fit_mean,statistic = "conf.int",
 There is a related package, `marginaleffects`, that allows us to convert these coefficients into more meaningful marginal effect estimates, i.e., the effect of the predictors expresses as the actual change in the outcome on the 0 - 1 scale. Because we have two variables in our model that are both ordinal in nature, `marginaleffects` will produce an estimate of the marginal effect of each value of each ordinal predictor. I use the `avg_slopes` function to convert the `ordbetareg` model to a data frame of marginal/conditional effects in the scale of the outcome that can be easily printed:
 
 ```{r marg_effect}
-library(marginaleffects)
 
 avg_slopes(ord_fit_mean, variables="education") %>%
   select(Variable="term",
@@ -415,14 +414,14 @@ As I explain in the paper, one of the main advantages of using a Beta regression
 
 The one change we need to make to fit this model is to add a formula predicting `phi` in the code below. Because we now have two formulas--one for the mean and one for dispersion--I use the `bf` function to indicate these two sub-models. I also need to specify `phi_reg` to be TRUE because some of the priors will change. 
 
-Because there is no need to model the mean, I leave the first formula as `therm ~ 0`. I don't specify 1 for an intercept because the ordered regression model has other intercepts (i.e., the cutpoints). I then specify a separate model for `phi` with an interaction between `age` and `sex` to see if these covariates are associated with dispersion.
+Because there is no need to model the mean, I leave the first formula as `therm ~ 1` with the 1 representing only an intercept, not a covariate. I then specify a separate model for `phi` with an interaction between `age` and `sex` to see if these covariates are associated with dispersion.
 
 
 ```{r run_brms_phi}
 
 if(run_model) {
   
-  ord_fit_phi <- ordbetareg(bf(therm ~ 0, 
+  ord_fit_phi <- ordbetareg(bf(therm ~ 1, 
                 phi ~ age + sex),
                 phi_reg = T,
                 data=model_data,