r-cran-susier 0.12.35+dfsg-1 source package in Ubuntu
Changelog
r-cran-susier (0.12.35+dfsg-1) unstable; urgency=medium * Team upload. * New upstream version * Standards-Version: 4.6.2 (routine-update) -- Andreas Tille <email address hidden> Sun, 26 Feb 2023 12:53:09 +0100
Upload details
- Uploaded by:
- Debian R Packages Maintainers
- Uploaded to:
- Sid
- Original maintainer:
- Debian R Packages Maintainers
- Architectures:
- all
- Section:
- misc
- Urgency:
- Medium Urgency
See full publishing history Publishing
Series | Published | Component | Section | |
---|---|---|---|---|
Oracular | release | universe | misc | |
Noble | release | universe | misc | |
Mantic | release | universe | misc |
Downloads
File | Size | SHA-256 Checksum |
---|---|---|
r-cran-susier_0.12.35+dfsg-1.dsc | 2.2 KiB | 8104a17fd1c168fff8c2726c4516e39bcf30a54bff67d8aa83a5aa7019dafb1a |
r-cran-susier_0.12.35+dfsg.orig.tar.xz | 1.5 MiB | 9f2fae05b3511428e9a27f9cd60adf7419b80a711d9319d24a7561e38816db4b |
r-cran-susier_0.12.35+dfsg-1.debian.tar.xz | 3.7 KiB | 8a66c22d04b39c3d12e7f4702f82d753d3afeeace977d8ce6252782a73113047 |
Available diffs
- diff from 0.12.27+dfsg-1 to 0.12.35+dfsg-1 (12.5 KiB)
No changes file available.
Binary packages built by this source
- r-cran-susier: GNU R 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).