A multi-task learning approach to variable selection regression with highly correlated predictors and sparse effects, based on frequentist statistical inference. It provides statistical evidence to identify which subsets of predictors have non-zero effects on which subsets of response variables, motivated and designed for colocalization analysis across genome-wide association studies (GWAS) and quantitative trait loci (QTL) studies. The ColocBoost model is described in Cao et. al. (2025) <doi:10.1101/2025.04.17.25326042>.
Version: | 1.0.4 |
Depends: | R (≥ 4.0.0) |
Imports: | Rfast, matrixStats |
Suggests: | testthat (≥ 3.0.0), knitr, rmarkdown, ashr, MASS, susieR |
Published: | 2025-05-02 |
Author: | Xuewei Cao [cre, aut, cph], Haochen Sun [aut, cph], Ru Feng [aut, cph], Daniel Nachun [aut, cph], Kushal Dey [aut, cph], Gao Wang [aut, cph] |
Maintainer: | Xuewei Cao <xc2270 at cumc.columbia.edu> |
BugReports: | https://github.com/StatFunGen/colocboost/issues |
License: | MIT + file LICENSE |
URL: | https://github.com/StatFunGen/colocboost |
NeedsCompilation: | no |
Citation: | colocboost citation info |
CRAN checks: | colocboost results |
Package source: | colocboost_1.0.4.tar.gz |
Windows binaries: | r-devel: not available, r-release: not available, r-oldrel: not available |
macOS binaries: | r-release (arm64): colocboost_1.0.4.tgz, r-oldrel (arm64): colocboost_1.0.4.tgz, r-release (x86_64): colocboost_1.0.4.tgz, r-oldrel (x86_64): colocboost_1.0.4.tgz |
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