CRAN/E | SSVS

SSVS

Functions for Stochastic Search Variable Selection (SSVS)

Installation

About

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.

github.com/sabainter/SSVS
Bug report File report

Key Metrics

Version 2.0.0
R ≥ 2.10
Published 2022-05-29 670 days ago
Needs compilation? no
License GPL-3
CRAN checks SSVS results

Downloads

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Last 30 days 188 -6%
Last 90 days 765 +18%
Last 365 days 2.670 -1%

Maintainer

Maintainer

Sierra Bainter

sbainter@miami.edu

Authors

Sierra Bainter

cre / aut

Thomas McCauley

aut

Mahmoud Fahmy

aut

Dean Attali

aut

Material

README
NEWS
Reference manual
Package source

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

SSVS archive

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