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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>.
Getting started
Browse package contents
Package details |
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| Author | Sierra Bainter [cre, aut] (<https://orcid.org/0000-0001-7461-0803>), Thomas McCauley [aut], Mahmoud Fahmy [aut], Dean Attali [aut] (<https://orcid.org/0000-0002-5645-3493>) |
| Maintainer | Sierra Bainter <sbainter@miami.edu> |
| License | GPL-3 |
| Version | 2.1.0 |
| URL | https://github.com/sabainter/SSVS |
| Package repository | View on CRAN |
| Installation |
Install the latest version of this package by entering the following in R:
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Try the SSVS package in your browser
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