Package: hierGWAS 1.43.0

Laura Buzdugan

hierGWAS: Asessing statistical significance in predictive GWA studies

Testing individual SNPs, as well as arbitrarily large groups of SNPs in GWA studies, using a joint model of all SNPs. The method controls the FWER, and provides an automatic, data-driven refinement of the SNP clusters to smaller groups or single markers.

Authors:Laura Buzdugan

hierGWAS_1.43.0.tar.gz
hierGWAS_1.43.0.zip(r-4.7)hierGWAS_1.43.0.zip(r-4.6)hierGWAS_1.43.0.zip(r-4.5)
hierGWAS_1.43.0.tgz(r-4.6-any)hierGWAS_1.43.0.tgz(r-4.5-any)
hierGWAS_1.43.0.tar.gz(r-4.7-any)hierGWAS_1.43.0.tar.gz(r-4.6-any)
hierGWAS_1.43.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
hierGWAS/json (API)

# Install 'hierGWAS' in R:

install.packages('hierGWAS', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Datasets:

simGWAS - Simulated GWAS data

On BioConductor:hierGWAS-1.43.0(bioc 3.24)hierGWAS-1.42.0(bioc 3.23)

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

snp linkagedisequilibrium clustering

3.30 score 1 scripts 4 exports 12 dependencies

Last updated from:ff417c38bc. Checks:1 ERROR, 7 WARNING, 2 OK. Indexed: yes.

Target	Result	Time
bioc-checks	ERROR	141
linux-devel-x86_64	WARNING	179
source / vignettes	OK	204
linux-release-x86_64	WARNING	174
macos-release-arm64	WARNING	81
macos-oldrel-arm64	WARNING	79
windows-devel	WARNING	86
windows-release	WARNING	101
windows-oldrel	WARNING	88
wasm-release	OK	128

Exports:cluster.snp compute.r2 multisplit test.hierarchy

Dependencies:codetools fastcluster fmsb foreach glmnet iterators lattice Matrix Rcpp RcppEigen shape survival

User manual for R-Package hierGWAS

Rendered fromhierGWAS.Rnwusingutils::Sweave

Last update: 2015-06-08
Started: 2015-06-08

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Hierarchical Clustering of SNP Data	cluster.snp
R2 computation	compute.r2
Asessing statistical significance in predictive GWA studies	hierGWAS-package hierGWAS
Variable Selection on Random Sample Splits.	multisplit
Simulated GWAS data	simGWAS
Hierarchical Testing of SNPs	test.hierarchy