update comments for submission

.Rbuildignore

@@ -32,3 +32,4 @@ CONTRIBUTING.md
 ^pkgdown$
 ^CRAN-SUBMISSION$
 ^SECURITY\.md$
+^revdep$

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: BAS
-Version: 1.6.5
-Date: 2023-12-02
+Version: 1.6.6
+Date: 2023-11-28
 Title: Bayesian Variable Selection and Model Averaging using Bayesian Adaptive Sampling
 Authors@R: c(person("Merlise", "Clyde", email="[email protected]",
 	   		       role=c("aut","cre", "cph"),

NEWS.md

@@ -1,13 +1,14 @@
-# BAS 1.6.5
+# BAS 1.6.6
 
-## Changes
+## New Features
 
 * Added support for `Gamma` regression for `bas.glm`, with unit tests and 
   example  (Code contributed by @betsyberrson)
 
-* added error if initial model for the `bas.lm` sampling methods "MCMC" and "MCMC+BAS" had prior probability zero.   
 
-## Bug Fixes
+## Minor Improvements and Fixes
+
+* added error if supplied initial model for the `bas.lm` sampling methods "MCMC" and "MCMC+BAS" had prior probability zero.  
 
 * fixed printing problems as identified via [checks](https://cran.r-project.org/web/checks/check_results_BAS.html)
 

cran-comments.md

@@ -1,4 +1,4 @@
-# BAS 1.6.5 Comments to CRAN
+# BAS 1.6.6 Comments to CRAN
 
 # Notes to CRAN
 
@@ -36,4 +36,11 @@ and unit tests added
 
 * ginormal
 
+### revdepcheck results
+
+We checked 1 reverse dependencies, comparing R CMD check results across CRAN and dev versions of this package.
+
+ * We saw 0 new problems
+ * We failed to check 0 packages
+
 

revdep/.gitignore

@@ -0,0 +1,7 @@
+checks
+library
+checks.noindex
+library.noindex
+cloud.noindex
+data.sqlite
+*.html

revdep/README.md

@@ -0,0 +1,24 @@
+# Platform
+
+|field    |value                                                                                      |
+|:--------|:------------------------------------------------------------------------------------------|
+|version  |R version 4.3.2 (2023-10-31)                                                               |
+|os       |macOS Big Sur 11.7.10                                                                      |
+|system   |x86_64, darwin20                                                                           |
+|ui       |RStudio                                                                                    |
+|language |(EN)                                                                                       |
+|collate  |en_US.UTF-8                                                                                |
+|ctype    |en_US.UTF-8                                                                                |
+|tz       |America/New_York                                                                           |
+|date     |2023-11-28                                                                                 |
+|rstudio  |2023.09.1+494 Desert Sunflower (desktop)                                                   |
+|pandoc   |3.1.1 @ /Applications/RStudio.app/Contents/Resources/app/quarto/bin/tools/ (via rmarkdown) |
+
+# Dependencies
+
+|package |old   |new   |Δ  |
+|:-------|:-----|:-----|:--|
+|BAS     |1.6.4 |1.6.6 |*  |
+
+# Revdeps
+

revdep/cran.md

@@ -0,0 +1,7 @@
+## revdepcheck results
+
+We checked 1 reverse dependencies, comparing R CMD check results across CRAN and dev versions of this package.
+
+ * We saw 0 new problems
+ * We failed to check 0 packages
+

revdep/failures.md

@@ -0,0 +1 @@
+*Wow, no problems at all. :)*

revdep/problems.md

@@ -0,0 +1 @@
+*Wow, no problems at all. :)*

run_checks.R

@@ -1,3 +1,5 @@
+usethis::use_release_issue()
+
 # check email for results
 devtools::check_win_devel()
 devtools::check_win_release()

vignettes/BAS-vignette.Rmd

@@ -23,13 +23,13 @@ The `BAS` package provides easy to use functions to implement Bayesian Model Ave
 Prior distributions on coefficients are
 based on Zellner's g-prior or mixtures of g-priors, such as the 
 Zellner-Siow Cauchy prior or mixtures of g-priors from 
-[Liang et al (2008)](https://dx.doi.org/10.1198/016214507000001337)
+[Liang et al (2008)](https://doi.org/10.1198/016214507000001337)
 for linear models, as well as other options including AIC, BIC, RIC and Empirical Bayes methods.  Extensions to Generalized Linear Models are based on the 
 mixtures of g-priors in GLMs of
-[Li and Clyde (2019)](https://dx.doi.org/10.1080/01621459.2018.1469992) using an 
+[Li and Clyde (2019)](https://doi.org/10.1080/01621459.2018.1469992) using an 
 integrated Laplace approximation.
 
-`BAS` uses an adaptive sampling algorithm  to sample without replacement from the space of models or MCMC sampling which is recommended for sampling problems with a large number of predictors. See [Clyde, Littman & Ghosh](https://dx.doi.org/10.1198/jcgs.2010.09049) for more details for the sampling algorithms.
+`BAS` uses an adaptive sampling algorithm  to sample without replacement from the space of models or MCMC sampling which is recommended for sampling problems with a large number of predictors. See [Clyde, Littman & Ghosh](https://doi.org/10.1198/jcgs.2010.09049) for more details for the sampling algorithms.
 
 ## Installing BAS
 
@@ -291,9 +291,9 @@ new.pred <- predict(crime.ZS, newdata = UScrime, estimator = "MPM")
 
 ## Alternative algorithms
 
-`BAS` has several options for sampling from the model space with or without enumeration.  The (current) default `method="BAS"` samples models without replacement using estimates of the marginal inclusion probabilities using the algorithm described in  [Clyde et al (2011)](https://dx.doi.org/10.1198/jcgs.2010.09049).  The initial sampling probabilities provided by `initprobs` are updated based on the sampled models, every `update` iterations.
+`BAS` has several options for sampling from the model space with or without enumeration.  The (current) default `method="BAS"` samples models without replacement using estimates of the marginal inclusion probabilities using the algorithm described in  [Clyde et al (2011)](https://doi.org/10.1198/jcgs.2010.09049).  The initial sampling probabilities provided by `initprobs` are updated based on the sampled models, every `update` iterations.
 This can be more efficient in some cases if a large fraction of the model space has been sampled, however, in cases of high correlation and a large number of predictors, this can lead to biased estimates
-[Clyde and Ghosh (2012)](https://dx.doi.org/10.1093/biomet/ass040), in which case MCMC is preferred.  The `method="MCMC"` is described below and is better for large $p$. 
+[Clyde and Ghosh (2012)](https://doi.org/10.1093/biomet/ass040), in which case MCMC is preferred.  The `method="MCMC"` is described below and is better for large $p$. 
 
 A deterministic sampling scheme is also available for enumeration;