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 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 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 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.

MASS, knitr, ggplot2, GGally, rmarkdown, roxygen2, dplyr,
glmbb, testthat, covr

BASIX: An efficient C/C++ toolset for R.
BASIX provides some efficient C/C++ implementations to speed up calculations in R.

Accelerated computation for some basic R functions.

Bayesian fitting and sensitivity analysis methods for adaptive
spline surfaces. Built to handle continuous and categorical inputs as well as
functional or scalar output. An extension of the methodology in Denison, Mallick
and Smith (1998) <doi:10.1023/A:1008824606259>.
c(person("Devin", "Francom", role = c("aut", "cre"), email = "devinfrancom@gmail.com"),
person("Bruno", "Sanso", role = "ths"),
person("Agner", "Fog", role = c("ctb","cph")))
Devin Francom [aut, cre],
Bruno Sanso [ths],
Agner Fog [ctb, cph]