hibayes: Individual-Level, Summary-Level and Single-Step Bayesian Regression Model

A user-friendly tool to fit Bayesian regression models. It can fit 3 types of Bayesian models using individual-level, summary-level, and individual plus pedigree-level (single-step) data for both Genomic prediction/selection (GS) and Genome-Wide Association Study (GWAS), it was designed to estimate joint effects and genetic parameters for a complex trait, including: (1) fixed effects and coefficients of covariates, (2) environmental random effects, and its corresponding variance, (3) genetic variance, (4) residual variance, (5) heritability, (6) genomic estimated breeding values (GEBV) for both genotyped and non-genotyped individuals, (7) SNP effect size, (8) phenotype/genetic variance explained (PVE) for single or multiple SNPs, (9) posterior probability of association of the genomic window (WPPA), (10) posterior inclusive probability (PIP). The functions are not limited, we will keep on going in enriching it with more features. References: Meuwissen et al. (2001) <doi:10.1093/genetics/157.4.1819>; Gustavo et al. (2013) <doi:10.1534/genetics.112.143313>; Habier et al. (2011) <doi:10.1186/1471-2105-12-186>; Yi et al. (2008) <doi:10.1534/genetics.107.085589>; Zhou et al. (2013) <doi:10.1371/journal.pgen.1003264>; Moser et al. (2015) <doi:10.1371/journal.pgen.1004969>; Lloyd-Jones et al. (2019) <doi:10.1038/s41467-019-12653-0>; Henderson (1976) <doi:10.2307/2529339>; Fernando et al. (2014) <doi:10.1186/1297-9686-46-50>.

Version: 1.1.0
Depends: R (≥ 3.3.0), bigmemory, Matrix
Imports: utils, stats, methods, Rcpp
LinkingTo: Rcpp, RcppArmadillo (≥ 0.9.600.0.0), RcppProgress, BH, bigmemory, Matrix
Published: 2022-05-25
Author: Lilin Yin [aut, cre, cph], Haohao Zhang [aut, cph], Xiaolei Liu [aut, cph]
Maintainer: Lilin Yin <ylilin at 163.com>
BugReports: https://github.com/YinLiLin/hibayes/issues
License: GPL-3
URL: https://github.com/YinLiLin/hibayes
NeedsCompilation: yes
SystemRequirements: C++11
CRAN checks: hibayes results

Documentation:

Reference manual: hibayes.pdf

Downloads:

Package source: hibayes_1.1.0.tar.gz
Windows binaries: r-devel: hibayes_1.0.1.zip, r-release: hibayes_1.0.1.zip, r-oldrel: hibayes_1.0.1.zip
macOS binaries: r-release (arm64): hibayes_1.0.1.tgz, r-oldrel (arm64): hibayes_1.0.1.tgz, r-release (x86_64): hibayes_1.0.1.tgz, r-oldrel (x86_64): hibayes_1.0.1.tgz
Old sources: hibayes archive

Linking:

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