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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 |
DOI: | 10.32614/CRAN.package.SSVS |
Author: | Sierra Bainter [cre, aut], Thomas McCauley [aut], Mahmoud Fahmy [aut], Dean Attali [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 NEWS |
CRAN checks: | SSVS results |
Reference manual: | SSVS.pdf |
Package source: | SSVS_2.0.0.tar.gz |
Windows binaries: | r-devel: SSVS_2.0.0.zip, r-release: SSVS_2.0.0.zip, r-oldrel: SSVS_2.0.0.zip |
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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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.