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SSVS  

Functions for Stochastic Search Variable Selection (SSVS)
View on CRAN: Click here


Download and install SSVS package within the R console
Install from CRAN:
install.packages("SSVS")

Install from Github:
library("remotes")
install_github("cran/SSVS")

Install by package version:
library("remotes")
install_version("SSVS", "2.1.0")



Attach the package and use:
library("SSVS")
Maintained by
Sierra Bainter
[Scholar Profile | Author Map]
First Published: 2022-03-08
Latest Update: 2022-05-29
Description:
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>.
How to cite:
Sierra Bainter (2022). SSVS: Functions for Stochastic Search Variable Selection (SSVS). R package version 2.1.0, https://cran.r-project.org/web/packages/SSVS. Accessed 22 Apr. 2025.
Previous versions and publish date:
1.0.0 (2022-03-08 21:20), 2.0.0 (2022-05-29 07:40)
Other packages that cited SSVS R package
View SSVS citation profile
Other R packages that SSVS depends, imports, suggests or enhances
Complete documentation for SSVS
Functions, R codes and Examples using the SSVS R package
Some associated functions: dat . launch . plot.ssvs . print.ssvs_summary . ssvs . summary.ssvs . 
Some associated R codes: SSVS.R . data.R . launch.R . plot.R . summary.R . utils.R .  Full SSVS package functions and examples
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