diff --git a/.Rbuildignore b/.Rbuildignore index fdf8f68e..7cc46295 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -32,3 +32,4 @@ CONTRIBUTING.md ^pkgdown$ ^CRAN-SUBMISSION$ ^SECURITY\.md$ +^revdep$ diff --git a/DESCRIPTION b/DESCRIPTION index 5c826a9e..6b616376 100644 --- a/DESCRIPTION +++ b/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="clyde@duke.edu", role=c("aut","cre", "cph"), diff --git a/NEWS.md b/NEWS.md index 4b63a8d8..18f1a4dd 100644 --- a/NEWS.md +++ b/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) diff --git a/cran-comments.md b/cran-comments.md index 2c2bb583..0620ba26 100644 --- a/cran-comments.md +++ b/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 + diff --git a/revdep/.gitignore b/revdep/.gitignore new file mode 100644 index 00000000..111ab324 --- /dev/null +++ b/revdep/.gitignore @@ -0,0 +1,7 @@ +checks +library +checks.noindex +library.noindex +cloud.noindex +data.sqlite +*.html diff --git a/revdep/README.md b/revdep/README.md new file mode 100644 index 00000000..18f03b6c --- /dev/null +++ b/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 + diff --git a/revdep/cran.md b/revdep/cran.md new file mode 100644 index 00000000..782ef684 --- /dev/null +++ b/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 + diff --git a/revdep/failures.md b/revdep/failures.md new file mode 100644 index 00000000..9a207363 --- /dev/null +++ b/revdep/failures.md @@ -0,0 +1 @@ +*Wow, no problems at all. :)* \ No newline at end of file diff --git a/revdep/problems.md b/revdep/problems.md new file mode 100644 index 00000000..9a207363 --- /dev/null +++ b/revdep/problems.md @@ -0,0 +1 @@ +*Wow, no problems at all. :)* \ No newline at end of file diff --git a/run_checks.R b/run_checks.R index 2aad49c3..cb1a9e39 100644 --- a/run_checks.R +++ b/run_checks.R @@ -1,3 +1,5 @@ +usethis::use_release_issue() + # check email for results devtools::check_win_devel() devtools::check_win_release() diff --git a/vignettes/BAS-vignette.Rmd b/vignettes/BAS-vignette.Rmd index 13d88048..8fdad018 100644 --- a/vignettes/BAS-vignette.Rmd +++ b/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;