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) and Zou et al (2021) . 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).


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0.11.92 by Peter Carbonetto, 2 months ago

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Authors: Gao Wang [aut] , Yuxin Zou [aut] , Kaiqian Zhang [aut] , Peter Carbonetto [aut, cre] , Matthew Stephens [aut]

Documentation:   PDF Manual  

MIT + file LICENSE license

Imports methods, graphics, grDevices, stats, Matrix, matrixStats, mixsqp, reshape, crayon, ggplot2

Suggests curl, testthat, microbenchmark, knitr, rmarkdown, L0Learn, genlasso, Rfast

Imported by coloc.

See at CRAN