SSVS: Functions for Stochastic Search Variable Selection (SSVS)

Functions for performing stochastic search variable selection (SSVS) for binary and continuous outcomes and visualizing the results. SSVS is a Bayesian variable selection method used to estimate the probability that individual predictors should be included in a regression model. Using MCMC estimation, the method samples thousands of regression models in order to characterize the model uncertainty regarding both the predictor set and the regression parameters.

Version: 1.0.0
Depends: R (≥ 2.10)
Imports: bayestestR, BoomSpikeSlab, checkmate, ggplot2, graphics, rlang, stats
Suggests: AER, testthat (≥ 3.0.0), knitr, rmarkdown
Published: 2022-03-08
Author: Sierra Bainter [cre, aut], Thomas McCauley [aut], Mahmoud Fahmy [aut], Dean Attali ORCID iD [aut]
Maintainer: Sierra Bainter <sbainter at miami.edu>
BugReports: https://github.com/sabainter/SSVS/issues
License: GPL-3
URL: https://github.com/sabainter/SSVS
NeedsCompilation: no
Materials: README
CRAN checks: SSVS results

Documentation:

Reference manual: SSVS.pdf

Downloads:

Package source: SSVS_1.0.0.tar.gz
Windows binaries: r-devel: SSVS_1.0.0.zip, r-release: SSVS_1.0.0.zip, r-oldrel: SSVS_1.0.0.zip
macOS binaries: r-release (arm64): SSVS_1.0.0.tgz, r-oldrel (arm64): SSVS_1.0.0.tgz, r-release (x86_64): SSVS_1.0.0.tgz, r-oldrel (x86_64): SSVS_1.0.0.tgz

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