Package: rMVP
Type: Package
Title: Memory-Efficient, Visualize-Enhanced, Parallel-Accelerated GWAS
        Tool
Version: 1.0.6
Date: 2021-04-17
Authors@R: c( person("Lilin", "Yin", role = "aut"), 
              person("Haohao", "Zhang", role = "aut"),
              person("Zhenshuang", "Tang", role = "aut"),
              person("Jingya", "Xu", role = "aut"),
              person("Dong", "Yin", role = "aut"),
              person("Zhiwu", "Zhang", role = "aut"),
              person("Xiaohui", "Yuan", role = "aut"),
              person("Mengjin", "Zhu", role = "aut"),
              person("Shuhong", "Zhao", role = "aut"),
              person("Xinyun", "Li", role = "aut"),
              person("Qishan", "Wang", role = "ctb"),
              person("Feng", "Tian", role = "ctb"),
              person("Hyunmin", "Kang", role = "ctb"),
              person("Xiang", "Zhou", role = "ctb"),
              person("Xiaolei", "Liu", role = c("cre", "aut", "cph"), email = "xiaoleiliu@mail.hzau.edu.cn"))
Description: A memory-efficient, visualize-enhanced, parallel-accelerated Genome-Wide Association Study (GWAS) tool. It can
    (1) effectively process large data, 
    (2) rapidly evaluate population structure, 
    (3) efficiently estimate variance components several algorithms, 
    (4) implement parallel-accelerated association tests of markers three methods, 
    (5) globally efficient design on GWAS process computing, 
    (6) enhance visualization of related information. 
    'rMVP' contains three models GLM (Alkes Price (2006) <DOI:10.1038/ng1847>), MLM (Jianming Yu (2006) <DOI:10.1038/ng1702>) 
    and FarmCPU (Xiaolei Liu (2016) <doi:10.1371/journal.pgen.1005767>); variance components estimation methods EMMAX 
    (Hyunmin Kang (2008) <DOI:10.1534/genetics.107.080101>;), FaSTLMM (method: Christoph Lippert (2011) <DOI:10.1038/nmeth.1681>, 
    R implementation from 'GAPIT2': You Tang and Xiaolei Liu (2016) <DOI:10.1371/journal.pone.0107684> and 
    'SUPER': Qishan Wang and Feng Tian (2014) <DOI:10.1371/journal.pone.0107684>), and HE regression 
    (Xiang Zhou (2017) <DOI:10.1214/17-AOAS1052>).
License: Apache License 2.0
SystemRequirements: C++11
URL: https://github.com/xiaolei-lab/rMVP
BugReports: https://github.com/xiaolei-lab/rMVP/issues
Imports: utils, stats, methods, graphics, grDevices, MASS, Rcpp,
        bigmemory
Depends: R (>= 3.3)
LinkingTo: Rcpp, RcppArmadillo, RcppEigen, RcppProgress, BH, bigmemory
NeedsCompilation: yes
Suggests: knitr, testthat, rmarkdown
RoxygenNote: 6.1.1
Maintainer: Xiaolei Liu <xiaoleiliu@mail.hzau.edu.cn>
Author: Lilin Yin [aut],
  Haohao Zhang [aut],
  Zhenshuang Tang [aut],
  Jingya Xu [aut],
  Dong Yin [aut],
  Zhiwu Zhang [aut],
  Xiaohui Yuan [aut],
  Mengjin Zhu [aut],
  Shuhong Zhao [aut],
  Xinyun Li [aut],
  Qishan Wang [ctb],
  Feng Tian [ctb],
  Hyunmin Kang [ctb],
  Xiang Zhou [ctb],
  Xiaolei Liu [cre, aut, cph]
Packaged: 2021-04-18 07:39:39 UTC; hyacz
Repository: CRAN
Date/Publication: 2021-04-18 14:50:09 UTC
Built: R 3.6.3; x86_64-w64-mingw32; 2021-05-12 19:44:08 UTC; windows
Archs: i386, x64
