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.

Last 30 days

This package has been downloaded 335 times in the last 30 days. Now we're getting somewhere! Enough downloads to populate a lively group chat. The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 3 times.

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Last 365 days

This package has been downloaded 2,651 times in the last 365 days. Now we’re talking! This work is officially 'heard of in academic circles', just like those wild research papers on synthetic bananas. The day with the most downloads was Mar 24, 2025 with 47 downloads.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.