Package: milorGWAS 0.7

Hervé Perdry

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>.

Authors:Hervé Perdry [aut, cre], Jacqueline Milet [aut]

milorGWAS_0.7.tar.gz
milorGWAS_0.7.tar.gz(r-4.5-noble)milorGWAS_0.7.tar.gz(r-4.4-noble)
milorGWAS_0.7.tgz(r-4.4-emscripten)milorGWAS_0.7.tgz(r-4.3-emscripten)
milorGWAS.pdf |milorGWAS.html
milorGWAS/json (API)
NEWS

# Install 'milorGWAS' in R:
install.packages('milorGWAS', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • zlib– Compression library
  • c++– GNU Standard C++ Library v3

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.30 score 8 scripts 498 downloads 1 mentions 4 exports 4 dependencies

Last updated 6 months agofrom:4e24b9342b. Checks:OK: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 21 2024
R-4.5-linux-x86_64OKNov 21 2024

Exports:association.test.logisticassociation.test.logistic.dosageqqplot.pvaluesSNP.category

Dependencies:gastonRcppRcppEigenRcppParallel

milorGWAS package

Rendered frommilorGWAS.Rmdusingknitr::rmarkdownon Nov 21 2024.

Last update: 2024-06-22
Started: 2020-03-25