diff --git a/DESCRIPTION b/DESCRIPTION index 9d0e0c00..6e11959b 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,6 +1,6 @@ Package: BAS -Version: 1.5.6 -Date: 2020-8-24 +Version: 1.6.0 +Date: 2020-11-09 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"), @@ -14,10 +14,10 @@ Authors@R: c(person("Merlise", "Clyde", email="clyde@duke.edu", Depends: R (>= 3.5) Imports: - stats, graphics, + grDevices, + stats, utils, - grDevices Suggests: MASS, knitr, @@ -26,23 +26,22 @@ Suggests: rmarkdown, roxygen2, dplyr, - pkgdown, testthat, covr -Description: Package for Bayesian Variable Selection and Model Averaging - in linear models and generalized linear models using stochastic or - deterministic sampling without replacement from posterior - distributions. Prior distributions on coefficients are - from Zellner's g-prior or mixtures of g-priors +Description: Bayesian Variable Selection and Model Averaging + in linear models and generalized linear models implemented using + prior distributions on coefficients based on + Zellner's g-prior or mixtures of g-priors corresponding to the Zellner-Siow Cauchy Priors or the mixture of g-priors from Liang et al (2008) for linear models or mixtures of g-priors from Li and Clyde (2019) in generalized linear models. Other model selection criteria include AIC, BIC and Empirical Bayes - estimates of g. Sampling probabilities may be updated based on the sampled - models using sampling w/out replacement or an efficient MCMC algorithm which - samples models using a tree structure of the model space + estimates of g. Models may be sampled using Markov Chain Monte + Carlo, a deterministic sampler (for enumeration) or + sampling without replacement. Sampling probabilities may be updated based on + the sampled models using sampling w/out replacement using a tree structure of the model space as an efficient hash table. See Clyde, Ghosh and Littman (2010) for details on the sampling algorithms. Uniform priors over all models or beta-binomial prior distributions on