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 |
| 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 |
Documentation:
Downloads:
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