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

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>.

Version:	0.7
Depends:	gaston (≥ 1.6)
Imports:	Rcpp (≥ 1.0.2)
LinkingTo:	Rcpp, RcppEigen, gaston
Suggests:	knitr, rmarkdown, png
Published:	2024-06-21
DOI:	10.32614/CRAN.package.milorGWAS
Author:	Hervé Perdry [aut, cre], Jacqueline Milet [aut]
Maintainer:	Hervé Perdry <herve.perdry at universite-paris-saclay.fr>
License:	GPL-3
NeedsCompilation:	yes
Materials:	NEWS
CRAN checks:	milorGWAS results

Documentation:

Reference manual:	milorGWAS.pdf
Vignettes:	milorGWAS package

Downloads:

Package source:	milorGWAS_0.7.tar.gz
Windows binaries:	r-devel: milorGWAS_0.7.zip, r-release: milorGWAS_0.7.zip, r-oldrel: milorGWAS_0.7.zip
macOS binaries:	r-release (arm64): milorGWAS_0.7.tgz, r-oldrel (arm64): milorGWAS_0.7.tgz, r-release (x86_64): milorGWAS_0.7.tgz, r-oldrel (x86_64): milorGWAS_0.7.tgz
Old sources:	milorGWAS archive

Linking:

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