Package: BoomSpikeSlab
Version: 1.2.5
Date: 2022-05-24
Title: MCMC for Spike and Slab Regression
Author: Steven L. Scott <steve.the.bayesian@gmail.com>
Maintainer: Steven L. Scott <steve.the.bayesian@gmail.com>
Description: Spike and slab regression with a variety of residual error
distributions corresponding to Gaussian, Student T, probit, logit, SVM, and a
few others.  Spike and slab regression is Bayesian regression with prior
distributions containing a point mass at zero.  The posterior updates the
amount of mass on this point, leading to a posterior distribution that is
actually sparse, in the sense that if you sample from it many coefficients are
actually zeros.  Sampling from this posterior distribution is an elegant way
to handle Bayesian variable selection and model averaging.  See
<DOI:10.1504/IJMMNO.2014.059942> for an explanation of the Gaussian case.
License: LGPL-2.1 | file LICENSE
Depends: Boom (>= 0.9.8) , R (>= 3.5.0)
LinkingTo: Boom(>= 0.9.8)
Suggests: MASS, testthat, mlbench, igraph
Encoding: UTF-8
NeedsCompilation: yes
Packaged: 2022-05-26 14:22:58 UTC; steve
Repository: CRAN
Date/Publication: 2022-05-26 17:40:02 UTC

Title: Boosting for Multivariate Longitudinal Response
Description: Jointly models the multivariate longitudinal response and multiple covariates and time using gradient boosting approach.
Imports: Rcpp (>= 0.12.18), stats, splines, nlme

Title: Boosting for Multivariate Longitudinal Responses
Description: Jointly models the multivariate longitudinal responses and multiple covariates and time using gradient boosting approach.

Author: Eun-Kyung Lee, Samsun Sung, Heebal Kim,
Title:  Bootstrapping test for chromosomal localization
Description: Bootstrapping test for chromosomal localization
Packaged: Sun Jan  7 22:21:27 2007; hornik

Title: Bootstrap-MRMR Technique for Informative Gene Selection
Description: Selection of informative features like genes, transcripts, RNA seq, etc. using Bootstrap Maximum Relevance and Minimum Redundancy technique from a given high dimensional genomic dataset. Informative gene selection involves identification of relevant genes and removal of redundant genes as much as possible from a large gene space. Main applications in high-dimensional expression data analysis (e.g. microarray data, NGS expression data and other genomics and proteomics applications).
Packaged: 2016-09-12 06:47:39 UTC; samarendra