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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).
Version: | 0.12.35 |
Depends: | R (≥ 3.0.0) |
Imports: | methods, graphics, grDevices, stats, Matrix, matrixStats, mixsqp, reshape, crayon, ggplot2 |
Suggests: | curl, testthat, microbenchmark, knitr, rmarkdown, Rfast, cowplot |
Published: | 2023-02-17 |
DOI: | 10.32614/CRAN.package.susieR |
Author: | Gao Wang [aut], Yuxin Zou [aut], Kaiqian Zhang [aut], Peter Carbonetto [aut, cre], Matthew Stephens [aut] |
Maintainer: | Peter Carbonetto <peter.carbonetto at gmail.com> |
BugReports: | https://github.com/stephenslab/susieR/issues |
License: | BSD_3_clause + file LICENSE |
URL: | https://github.com/stephenslab/susieR |
NeedsCompilation: | no |
Citation: | susieR citation info |
Materials: | README |
CRAN checks: | susieR results |
Package source: | susieR_0.12.35.tar.gz |
Windows binaries: | r-devel: susieR_0.12.35.zip, r-release: susieR_0.12.35.zip, r-oldrel: susieR_0.12.35.zip |
macOS binaries: | r-release (arm64): susieR_0.12.35.tgz, r-oldrel (arm64): susieR_0.12.35.tgz, r-release (x86_64): susieR_0.12.35.tgz, r-oldrel (x86_64): susieR_0.12.35.tgz |
Old sources: | susieR archive |
Reverse imports: | coloc |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.