RAINBOWR: Genome-Wide Association Study with SNP-Set Methods

By using 'RAINBOWR' (Reliable Association INference By Optimizing Weights with R), users can test multiple SNPs (Single Nucleotide Polymorphisms) simultaneously by kernel-based (SNP-set) methods. This package can also be applied to haplotype-based GWAS (Genome-Wide Association Study). Users can test not only additive effects but also dominance and epistatic effects. In detail, please check our paper on PLOS Computational Biology: Kosuke Hamazaki and Hiroyoshi Iwata (2020) <doi:10.1371/journal.pcbi.1007663>.

Version: 0.1.21
Depends: R (≥ 3.5.0)
Imports: Rcpp, rgl, tcltk, Matrix, cluster, MASS, pbmcapply, optimx, methods, ape, stringr, pegas, ggplot2, ggtree, scatterpie, phylobase, haplotypes, ggimage, rrBLUP, expm, parallel, pbapply
LinkingTo: Rcpp, RcppEigen
Suggests: knitr, rmarkdown
Published: 2020-10-26
Author: Kosuke Hamazaki [aut, cre], Hiroyoshi Iwata [aut, ctb]
Maintainer: Kosuke Hamazaki <hamazaki at ut-biomet.org>
License: MIT + file LICENSE
NeedsCompilation: yes
Citation: RAINBOWR citation info
Materials: README
CRAN checks: RAINBOWR results


Reference manual: RAINBOWR.pdf
Vignettes: RAINBOWR
Package source: RAINBOWR_0.1.21.tar.gz
Windows binaries: r-devel: RAINBOWR_0.1.21.zip, r-release: RAINBOWR_0.1.21.zip, r-oldrel: RAINBOWR_0.1.19.zip
macOS binaries: r-release: RAINBOWR_0.1.19.tgz, r-oldrel: RAINBOWR_0.1.19.tgz
Old sources: RAINBOWR archive


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