Updated BAS to 1.5.5 from CRAN

Author: JorisGoosen

R/library/BAS/DESCRIPTION

@@ -1,6 +1,6 @@
 Package: BAS
-Version: 1.5.4
-Date: 2019-11-4
+Version: 1.5.5
+Date: 2020-1-24
 Title: Bayesian Variable Selection and Model Averaging using Bayesian
         Adaptive Sampling
 Authors@R: c(person("Merlise", "Clyde", email="clyde@duke.edu",
@@ -11,32 +11,36 @@ Authors@R: c(person("Merlise", "Clyde", email="clyde@duke.edu",
 	   person("Quanli", "Wang", role="ctb"),
 	   person("Joyee", "Ghosh", role="ctb"),
 	   person("Yingbo", "Li", role="ctb"),
-	   person("Don", "van de Berg", role="ctb"))
+	   person("Don", "van de Bergh", role="ctb"))
 Depends: R (>= 3.0)
 Imports: stats, graphics, utils, grDevices
-Suggests: MASS, knitr, GGally, rmarkdown, roxygen2, dplyr, glmbb,
-        pkgdown, testthat, covr
-Description: Package for Bayesian Variable Selection and  Model Averaging in linear models and
-    generalized linear models using stochastic or
+Suggests: MASS, knitr, ggplot2, GGally, rmarkdown, roxygen2, dplyr,
+        glmbb, 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
     corresponding to the Zellner-Siow Cauchy Priors or the
     mixture of g-priors from Liang et al (2008)
     <DOI:10.1198/016214507000001337>
-    for linear models or mixtures of g-priors in GLMs of Li and Clyde (2018)
-    <arXiv:1503.06913>. 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
-    samples models using the BAS tree structure as an efficient hash table.
+    for linear models or mixtures of g-priors from  Li and Clyde
+    (2019) <DOI:10.1080/01621459.2018.1469992> 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 
+    as an efficient hash table.  See  Clyde, Ghosh and Littman (2010) 
+    <DOI:10.1198/jcgs.2010.09049> for  details on the sampling algorithms.
     Uniform priors over all models or beta-binomial prior distributions on
     model size are allowed, and for large p truncated priors on the model
-    space may be used.  The user may force variables to always be included.
-    Details behind the sampling algorithm are provided in
-    Clyde, Ghosh and Littman (2010)   <DOI:10.1198/jcgs.2010.09049>.
+    space may be used to enforce sampling models that are full rank.  
+    The user may force variables to always be included in addition to imposing
+    constraints that higher order interactions are included only if their 
+    parents are included in the model.
     This material is based upon work supported by the National Science
-    Foundation under Grant DMS-1106891.  Any opinions, findings, and
+    Foundation under Division of Mathematical Sciences grant 1106891.
+    Any opinions, findings, and
     conclusions or recommendations expressed in this material are those of
     the author(s) and do not necessarily reflect the views of the
     National Science Foundation.
@@ -48,24 +52,15 @@ NeedsCompilation: yes
 ByteCompile: yes
 VignetteBuilder: knitr
 Encoding: UTF-8
-RoxygenNote: 6.1.1
-RemoteType: github
-RemoteHost: api.github.com
-RemoteRepo: BAS
-RemoteUsername: vandenman
-RemoteRef: jaspChanges
-RemoteSha: cc28ab75e4276eb0b13f4ae89a9fa88216581ed0
-GithubRepo: BAS
-GithubUsername: vandenman
-GithubRef: jaspChanges
-GithubSHA1: cc28ab75e4276eb0b13f4ae89a9fa88216581ed0
-Packaged: 2019-11-13 12:17:34 UTC; Frans Meerhoff
+RoxygenNote: 7.0.2
+Packaged: 2020-01-24 21:57:29 UTC; mclyde
 Author: Merlise Clyde [aut, cre, cph] (ORCID=0000-0002-3595-1872),
   Michael Littman [ctb],
   Quanli Wang [ctb],
   Joyee Ghosh [ctb],
   Yingbo Li [ctb],
-  Don van de Berg [ctb]
+  Don van de Bergh [ctb]
 Maintainer: Merlise Clyde <clyde@duke.edu>
-Built: R 3.6.1; x86_64-w64-mingw32; 2019-11-13 12:17:43 UTC; windows
+Date/Publication: 2020-01-24 22:50:14 UTC
+Built: R 3.6.2; x86_64-w64-mingw32; 2020-02-11 03:29:41 UTC; windows
 Archs: i386, x64

R/library/BAS/INDEX

@@ -32,6 +32,7 @@ beta.binomial           Beta-Binomial Prior Distribution for Models
 beta.prime              Beta-Prime Prior Distribution for Coefficients
                         in BMA Model
 bodyfat                 Bodyfat Data
+climate                 Climate Data
 coef.bas                Coefficients of a Bayesian Model Average object
 confint.coef.bas        Compute Credible Intervals for BAS regression
                         coefficients from BAS objects