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).
| Version: |
0.12.27 |
| Depends: |
R (≥ 3.0.0) |
| Imports: |
methods, graphics, grDevices, stats, Matrix, matrixStats, mixsqp, reshape, crayon, ggplot2 |
| Suggests: |
curl, testthat, microbenchmark, knitr, rmarkdown, L0Learn, Rfast, cowplot |
| Published: |
2022-09-06 |
| 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 |
Documentation:
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