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. For details see Bainter, McCauley, Wager, and Losin (2020) Improving practices for selecting a subset of important predictors in psychology: An application to predicting pain, Advances in Methods and Practices in Psychological Science 3(1), 66-80 <doi:10.1177/2515245919885617>.

Version: 2.0.0
Depends: R (≥ 2.10)
Imports: bayestestR, BoomSpikeSlab, checkmate, ggplot2, graphics, rlang, stats
Suggests: AER, bslib, foreign, glue, knitr, psych, reactable, readxl, rmarkdown, scales, shiny, shinyjs, shinyWidgets, testthat (≥ 3.0.0), tools, utils
Published: 2022-05-29
Author: Sierra Bainter [cre, aut], Thomas McCauley [aut], Mahmoud Fahmy [aut], Dean Attali ORCID iD [aut]
Maintainer: Sierra Bainter <sbainter at>
License: GPL-3
NeedsCompilation: no
Materials: README NEWS
CRAN checks: SSVS results


Reference manual: SSVS.pdf


Package source: SSVS_2.0.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): SSVS_2.0.0.tgz, r-oldrel (arm64): SSVS_2.0.0.tgz, r-release (x86_64): SSVS_2.0.0.tgz, r-oldrel (x86_64): SSVS_2.0.0.tgz
Old sources: SSVS archive


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