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>. 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).

Version: 0.11.42
Depends: R (≥ 3.0.0)
Imports: methods, graphics, grDevices, stats, Matrix, mixsqp, reshape, ggplot2
Suggests: testthat, microbenchmark, knitr, rmarkdown, L0Learn, genlasso
Published: 2021-06-14
Author: Gao Wang [aut], Yuxin Zou [aut], Kaiqian Zhang [aut], Peter Carbonetto [aut, cre], Matthew Stephens [aut]
Maintainer: Peter Carbonetto <peter.carbonetto at>
License: MIT + file LICENSE
NeedsCompilation: no
Citation: susieR citation info
Materials: README
CRAN checks: susieR results


Reference manual: susieR.pdf
Vignettes: Fine-mapping with summary statistics
Fine-mapping example
L0Learn initialization demo
minimal example
Sparse vs. dense matrix operations
SuSiE with sparse matrix operations
Refine SuSiE model
Diagnostic for fine-mapping with summary statistics
Trend filtering demo
Implementation of SuSiE trend filtering
Package source: susieR_0.11.42.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): susieR_0.11.42.tgz, r-release (x86_64): susieR_0.11.42.tgz, r-oldrel: susieR_0.11.42.tgz
Old sources: susieR archive

Reverse dependencies:

Reverse imports: coloc


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