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rMVP  

Memory-Efficient, Visualize-Enhanced, Parallel-Accelerated GWAS Tool
View on CRAN: Click here


Download and install rMVP package within the R console
Install from CRAN:
install.packages("rMVP")

Install from Github:
library("remotes")
install_github("cran/rMVP")

Install by package version:
library("remotes")
install_version("rMVP", "1.1.1")



Attach the package and use:
library("rMVP")
Maintained by
Xiaolei Liu
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-08-30
Latest Update: 2023-09-01
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) ), MLM (Jianming Yu (2006) ) and FarmCPU (Xiaolei Liu (2016) ); variance components estimation methods EMMAX (Hyunmin Kang (2008) ;), FaSTLMM (method: Christoph Lippert (2011) , R implementation from 'GAPIT2': You Tang and Xiaolei Liu (2016) and 'SUPER': Qishan Wang and Feng Tian (2014) ), and HE regression (Xiang Zhou (2017) ).
How to cite:
Xiaolei Liu (2019). rMVP: Memory-Efficient, Visualize-Enhanced, Parallel-Accelerated GWAS Tool. R package version 1.1.1, https://cran.r-project.org/web/packages/rMVP. Accessed 06 Nov. 2024.
Previous versions and publish date:
0.99.15 (2019-08-30 09:30), 0.99.16 (2019-09-24 12:10), 0.99.17 (2019-10-18 15:10), 0.99.18 (2020-03-19 19:00), 1.0.0 (2020-05-07 17:10), 1.0.3 (2020-07-17 10:30), 1.0.4 (2020-09-01 14:30), 1.0.5 (2021-03-02 16:40), 1.0.6 (2021-04-18 16:50), 1.0.7 (2023-09-01 10:30), 1.0.8 (2023-11-27 10:20)
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