susieR: Sum of Single Effects Linear Regression

Implements methods for variable selection in linear regression based on the "Sum of Single Effects" (SuSiE) model, as described in Wang et al (2020) <DOI:10.1101/501114> and Zou et al (2021) <DOI:10.1101/2021.11.03.467167>. These methods provide simple summaries, called "Credible Sets", for accurately quantifying uncertainty in which variables should be selected. The methods are motivated by genetic fine-mapping applications, and are particularly well-suited to settings where variables are highly correlated and detectable effects are sparse. The fitting algorithm, a Bayesian analogue of stepwise selection methods called "Iterative Bayesian Stepwise Selection" (IBSS), is simple and fast, allowing the SuSiE model be fit to large data sets (thousands of samples and hundreds of thousands of variables).

Package details

AuthorGao Wang [aut], Yuxin Zou [aut], Kaiqian Zhang [aut], Peter Carbonetto [aut, cre], Matthew Stephens [aut]
MaintainerPeter Carbonetto <>
LicenseBSD_3_clause + file LICENSE
Package repositoryView on CRAN
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susieR documentation built on March 7, 2023, 6:11 p.m.