Package: SAIGEgds
Type: Package
Title: Scalable Implementation of Generalized mixed models using GDS
        files in Phenome-Wide Association Studies
Version: 1.4.0
Date: 2020-10-21
Depends: R (>= 3.5.0), gdsfmt (>= 1.20.0), SeqArray (>= 1.24.1), Rcpp
LinkingTo: Rcpp, RcppArmadillo, RcppParallel
Imports: methods, stats, utils, RcppParallel, SPAtest (>= 3.0.0)
Suggests: parallel, crayon, RUnit, knitr, BiocGenerics, SNPRelate
Authors@R: c(person("Xiuwen", "Zheng", role=c("aut", "cre"),
        email="xiuwen.zheng@abbvie.com", comment=c(ORCID="0000-0002-1390-0708")),
        person("Wei", "Zhou", role="ctb",
        comment="the original author of the SAIGE R package"),
        person("J. Wade", "Davis", role="ctb"))
Description: Scalable implementation of generalized mixed models with highly
        optimized C++ implementation and integration with Genomic Data Structure
        (GDS) files. It is designed for single variant tests in large-scale
        phenome-wide association studies (PheWAS) with millions of variants and
        samples, controlling for sample structure and case-control imbalance.
        The implementation is based on the original SAIGE R package (v0.29.4.4).
        SAIGEgds also implements some of the SPAtest functions in C to speed up
        the calculation of Saddlepoint approximation. Benchmarks show that
        SAIGEgds is 5 to 6 times faster than the original SAIGE R package.
License: GPL-3
SystemRequirements: C++11, GNU make
VignetteBuilder: knitr
ByteCompile: TRUE
URL: https://github.com/AbbVie-ComputationalGenomics/SAIGEgds
biocViews: Software, Genetics, StatisticalMethod
git_url: https://git.bioconductor.org/packages/SAIGEgds
git_branch: RELEASE_3_12
git_last_commit: 6d29973
git_last_commit_date: 2020-10-27
Date/Publication: 2020-10-27
NeedsCompilation: yes
Packaged: 2020-10-28 04:06:35 UTC; biocbuild
Author: Xiuwen Zheng [aut, cre] (<https://orcid.org/0000-0002-1390-0708>),
  Wei Zhou [ctb] (the original author of the SAIGE R package),
  J. Wade Davis [ctb]
Maintainer: Xiuwen Zheng <xiuwen.zheng@abbvie.com>
Built: R 4.0.3; i386-w64-mingw32; 2020-10-28 15:13:06 UTC; windows
Archs: i386, x64
