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 finemapping applications, and are particularly wellsuited 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 


Author  Gao Wang [aut], Yuxin Zou [aut], Kaiqian Zhang [aut], Peter Carbonetto [aut, cre], Matthew Stephens [aut] 
Maintainer  Peter Carbonetto <peter.carbonetto@gmail.com> 
License  BSD_3_clause + file LICENSE 
Version  0.12.35 
URL  https://github.com/stephenslab/susieR 
Package repository  View on CRAN 
Installation 
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