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milorGWAS

Mixed Logistic Regression for Genome-Wide Analysis Studies (GWAS)

v0.7.1 · Aug 26, 2025 · GPL-3

Description

Fast approximate methods for mixed logistic regression in genome-wide analysis studies (GWAS). Two computationnally efficient methods are proposed for obtaining effect size estimates (beta) in Mixed Logistic Regression in GWAS: the Approximate Maximum Likelihood Estimate (AMLE), and the Offset method. The wald test obtained with AMLE is identical to the score test. Data can be genotype matrices in plink format, or dosage (VCF files). The methods are described in details in Milet et al (2020) <doi:10.1101/2020.01.17.910109>.

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NOTE 12 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Mar 9, 2026
NOTE r-oldrel-macos-arm64

installed package size

  installed size is 15.0Mb
  sub-directories of 1Mb or more:
    libs  14.4Mb
NOTE r-oldrel-macos-x86_64

installed package size

  installed size is 15.1Mb
  sub-directories of 1Mb or more:
    libs  14.4Mb

Dependency Network

Dependencies Reverse dependencies gaston Rcpp milorGWAS

Version History

new 0.7.1 Mar 9, 2026