This package is considered a duplicate. The official version of this package is found at:https://psoerensen.r-universe.dev/qgg

Package: qgg 1.1.6

Peter Soerensen

qgg: Statistical Tools for Quantitative Genetic Analyses

Provides an infrastructure for efficient processing of large-scale genetic and phenotypic data including core functions for: 1) fitting linear mixed models, 2) constructing marker-based genomic relationship matrices, 3) estimating genetic parameters (heritability and correlation), 4) performing genomic prediction and genetic risk profiling, and 5) single or multi-marker association analyses. Rohde et al. (2019) <doi:10.1101/503631>.

Authors:Peter Soerensen [aut, cre], Palle Duun Rohde [aut], Izel Fourie Soerensen [aut]

qgg_1.1.6.tar.gz
qgg_1.1.6.tar.gz(r-4.5-noble)qgg_1.1.6.tar.gz(r-4.4-noble)
qgg.pdf |qgg.html✨
qgg/json (API)
NEWS

# Install 'qgg' in R:

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

Bug tracker:https://github.com/psoerensen/qgg/issues

Uses libs:

openblas– Optimized BLAS
fortran– Runtime library for GNU Fortran applications
c++– GNU Standard C++ Library v3

fortran openblas cpp

2.96 score 46 scripts 364 downloads 2 mentions 52 exports 15 dependencies

Last updated 14 days agofrom:0f0aebc63c. Checks:OK: 2. Indexed: no.

Target	Result	Date
Doc / Vignettes	OK	Dec 16 2024
R-4.5-linux-x86_64	OK	Dec 16 2024

Exports:acc adjLD adjLDStat adjStat adjustB adjustMapLD checkStat computeROC createLDsets gbayes gblup getG getGRM getLD getLDscores getLDsets getMap getMarkers getPos getSparseLD gfilter glma gmap gprep greml grm gscore gsea gsim gsimC gsimR gsolve ldsc ldscore magma mapSets mapStat mergeGRM mtadj plotBayes plotForest plotLD plotROC pops predict_auc_mt_cc predict_auc_mt_continuous predict_auc_st predict_r2_mt predict_r2_st splitWithOverlap vegas writeBED

Dependencies:coda corpcor data.table lattice MASS Matrix MatrixModels mcmc MCMCpack quantreg Rcpp RcppArmadillo SparseM statmod survival

Citation

Readme and manuals

Help Manual

Help page	Topics
Compute prediction accuracy for a quantitative or binary trait	acc
Adjustment of marker summary statistics using clumping and thresholding	adjStat
Bayesian linear regression models	gbayes
Get elements from genotype matrix stored in PLINK bedfiles	getG
Filter genetic marker data based on different quality measures	gfilter
Single marker association analysis using linear models or linear mixed models	glma
Finemapping using Bayesian Linear Regression Models	gmap
Prepare genotype data for all statistical analyses	gprep
Genomic rescticted maximum likelihood (GREML) analysis	greml
Computing the genomic relationship matrix (GRM)	grm
Genomic scoring based on single marker summary statistics	gscore
Gene set enrichment analysis	gsea
Genomic simulation	gsim
Solve linear mixed model equations	gsolve
LD score regression	ldsc
Bayesian Multi-marker Analysis of Genomic Annotation (Bayesian MAGMA)	magma
Adjustment of marker effects using correlated trait information	mtadj
Bayesian Polygenic Prioritisation Scoring (Bayesian POPS)	pops
Perform VEGAS Gene-Based Association Analysis	vegas

Package: qgg 1.1.6

qgg: Statistical Tools for Quantitative Genetic Analyses

Citation

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)