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  [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:
Downloads:
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