colocboost
1.0.9Multi-Context Colocalization Analysis for QTL and GWAS Studies
Overview
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.
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- OK2026-06-0813 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-06-0711 OK · 0 NOTE · 0 WARNING · 2 ERROR · 0 FAILURE
- OK2026-03-1014 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
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Package metadata
- First published
- 2025-05-02
- Total releases
- 6 / 1 yrs
- License
- MIT + file LICENSE OSI
- Minimum R
- ≥ 4.0.0
- Bundled data
- 2.7 MB / 6 files
- Download size
- 4.2 MB
- Installed size
- not tracked yet
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- not tracked yet