Package: bWGR 2.2.17

bWGR: Bayesian Whole-Genome Regression

Whole-genome regression methods on Bayesian framework fitted via EM or Gibbs sampling, single step (<doi:10.1534/g3.119.400728>), univariate and multivariate (<doi:10.1186/s12711-022-00730-w>, <doi:10.1093/genetics/iyae179>), with optional kernel term and sampling techniques (<doi:10.1186/s12859-017-1582-3>).

Authors:Alencar Xavier [aut, cre], William Muir [aut], David Habier [aut], Kyle Kocak [aut], Shizhong Xu [aut], Katy Rainey [aut]

bWGR_2.2.17.tar.gz
bWGR_2.2.17.zip(r-4.7)bWGR_2.2.17.zip(r-4.6)bWGR_2.2.17.zip(r-4.5)
bWGR_2.2.17.tgz(r-4.6-x86_64)bWGR_2.2.17.tgz(r-4.6-arm64)bWGR_2.2.17.tgz(r-4.5-x86_64)bWGR_2.2.17.tgz(r-4.5-arm64)
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bWGR_2.2.17.tgz(r-4.5-emscripten)
bWGR.pdf |bWGR.html✨
bWGR/json (API)

# Install 'bWGR' in R:
install.packages('bWGR', repos = c('https://alenxav.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/alenxav/bwgr/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • chr - Tetra-seed Pods
  • fam - Tetra-seed Pods
  • gen - Tetra-seed Pods
  • y - Tetra-seed Pods

On CRAN:

Conda:

cpp

4.27 score 8 stars 33 scripts 632 downloads 1 mentions 95 exports 4 dependencies

Last updated from:7d6b258fe4. Checks:12 OK, 1 NOTE. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK247
linux-devel-x86_64OK232
source / vignettesOK291
linux-release-arm64OK215
linux-release-x86_64OK221
macos-release-arm64OK339
macos-release-x86_64OK328
macos-oldrel-arm64OK276
macos-oldrel-x86_64OK454
windows-develOK500
windows-releaseNOTE285
windows-oldrelOK277
wasm-releaseOK178

Exports:AccByCBayesABayesA2BayesBBayesB2BayesCBayesCpiBayesDpiBayesLBayesRRBayesRR2ClusterBlupCNTEigenAccEigenARCEigenArcZEigenBDCSVDEigenCNTEigenEVDEigenGAUEigenGauZEigenGRMEigenJacobiSVDEM_reclusteremBAemBBemBCemBCpiemBLemCVemDEemENemGWAemMLemML2emRRFUVBETAGAUGet_Cluster_CorrGRMGS2EIGENGSEMGSFLMGSRRHmatIMPIncMatrixK2XKMUPKMUP2lassomarkovmcmcCVMEGAmixedmkrmkr2XMLMmmmrrmrr_floatmrr_svdmrr2XMRR3MRR3FmtgsrumtmixedMvSimYmwgrNNSNNSEARCHPEGSPEGS_sparsePEGSXPEGSZPEGSZ_sparsepredict_FLMSSSEMSibZSimGCSimYSimZsolver1xsolver1xFsolver2xsolver2xFSPCSPMUVBETAwgrXFUVBETAXSEMFYSEMFZFUVBETAZSEMF

Dependencies:latticeMatrixRcppRcppEigen

Readme and manuals

Help Manual

Help pageTopics
Bayesian Whole-Genome RegressionbWGR-package bWGR
Tetra-seed Podschr fam gen tpod y
MCMC Whole-genome RegressionBayesA BayesA2 BayesB BayesB2 BayesC BayesCpi BayesDpi BayesL BayesRR BayesRR2 GSEN KMUP KMUP2 mcmcCV wgr
Expectation-Maximization WGRemBA emBB emBC emBCpi emBL emCV emDE emEN emML emML2 emRR lasso
Multivariate RegressionClusterBlup EM_recluster FUVBETA Get_Cluster_Corr GSEM IncMatrix MEGA mkr mkr2X MLM mrr mrr2X MRR3 MRR3F mrr_float mrr_svd mwgr PEGS PEGSX PEGSZ PEGSZ_sparse PEGS_sparse SEM solver1x solver1xF solver2x solver2xF UVBETA XFUVBETA XSEMF YSEMF ZFUVBETA ZSEMF
Mixed model solverGS2EIGEN GSFLM GSRR mixed mm mtgsru mtmixed NNS NNSEARCH predict_FLMSS
Additional toolsAccByC CNT EigenAcc EigenARC EigenArcZ EigenBDCSVD EigenCNT EigenEVD EigenGAU EigenGauZ EigenGRM EigenJacobiSVD emGWA GAU GRM Hmat IMP K2X markov MvSimY SibZ SimGC SimY SimZ SPC SPM