Multi-Context Colocalization Analysis for QTL and GWAS Studies R package colocboost version 1.0.9 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>. Software Depends: R (>= 4.0.0) Imports: Rfast, matrixStats Suggests: testthat (>= 3.0.0), knitr, rmarkdown, ashr, MASS, susieR Xuewei Cao <xc2270@cumc.columbia.edu> Comprehensive R Archive Network (CRAN) 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] MIT + file LICENSE (https://CRAN.R-project.org/package=colocboost/LICENSE) 2026-06-08 application/tgz https://CRAN.R-project.org/package=colocboost doi:10.32614/CRAN.package.colocboost