Description: Package for Bayesian Variable Selection and  Model Averaging
in linear models and generalized linear models using stochastic or
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.
The user may force variables to always be included in addition to imposing
constraints that higher order interactions are included only if their
Foundation under Division of Mathematical Sciences grant 1106891.

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

Description: Accelerated computation for some basic R functions.

Title: Bayesian Adaptive Spline Surfaces
Description: 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
Authors@R: c(person("Devin", "Francom", role = c("aut", "cre"), email = "devinfrancom@gmail.com"),
person("Agner", "Fog", role = c("ctb","cph")))

Suggests: knitr, testthat (>= 2.1.0), parallel, MASS, wmtsa, formatR

spline surfaces described in <doi:10.18637/jss.v094.i08>. Built to handle continuous and categorical inputs as well as
Suggests: knitr, testthat (>= 2.1.0), parallel, MASS, formatR

Title: Classification of mRNA and lncRNA Sequences using Complex
Maintainer: Eric Augusto Ito <ericaugustoito@hotmail.com>
Description: The methodology applied in this package starts with the creation of networks from RNA sequences, in this way the extraction of resources from these networks is performed with a threshold methodology, aiming to make a classification between the classes of the sequences. There are two data present in the package, "sequences" and "sequences2" with 11 and 10 sequences respectively. These sequences were taken from the data set used in the article (LI, Aimin; ZHANG, Junying; ZHOU, Zhongyin, 2014) <doi:10.1186/1471-2105-15-311>.
Imports: igraph, Biostrings, RWeka, randomForest, rgl, rmcfs,