updated description to drop pkgdown from suggested packages

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="[email protected]",
 	   		       role=c("aut","cre", "cph"),
@@ -14,10 +14,10 @@ Authors@R: c(person("Merlise", "Clyde", email="[email protected]",
 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)
     <DOI:10.1198/016214507000001337>
     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 
+    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) 
     <DOI:10.1198/jcgs.2010.09049> for  details on the sampling algorithms.
     Uniform priors over all models or beta-binomial prior distributions on