BMTME: Bayesian Multi-Trait Multi-Environment for Genomic Selection Analysis

Genomic selection and prediction models with the capacity to use multiple traits and environments, through ready-to-use Bayesian models. It consists a group of functions that help to create regression models for some genomic models proposed by Montesinos-López, et al. (2016) <doi:10.1534/g3.116.032359> also in Montesinos-López et al. (2018) <doi:10.1534/g3.118.200728> and Montesinos-López et al. (2018) <doi:10.2134/agronj2018.06.0362>.

Version: 1.0.4
Depends: R (≥ 3.0.0)
Imports: BGLR, doSNOW, dplyr, foreach, matrixcalc, mvtnorm, progress, snow, tidyr
LinkingTo: Rcpp, RcppArmadillo
Suggests: covr, knitr, rmarkdown, testthat
Published: 2019-01-11
Author: Francisco Javier Luna-Vazquez ORCID iD [aut, cre], Fernando H. Toledo [aut], Osval Antonio Montesinos-Lopez ORCID iD [aut], Abelardo Montesinos-Lopez [aut], Jose Crossa ORCID iD [aut]
Maintainer: Francisco Javier Luna-Vazquez <frahik at gmail.com>
BugReports: https://github.com/frahik/BMTME/issues/new
License: LGPL-3
URL: https://github.com/frahik/BMTME
NeedsCompilation: yes
SystemRequirements: C++11
Language: en-US
Materials: NEWS
CRAN checks: BMTME results

Downloads:

Reference manual: BMTME.pdf
Vignettes: Fit the Bayesian Multi-Environment model and apply a random partition cross-validation
Fit the Bayesian Multi-Output Regression Stacking model using the maize dataset and apply a random partition cross-validation
Fit the BMORS model for one environment of Maize dataset
Fit the Bayesian Multi-Trait Multi-Environment model and apply a random partition cross-validation
Package source: BMTME_1.0.4.tar.gz
Windows binaries: r-devel: BMTME_1.0.4.zip, r-release: BMTME_1.0.4.zip, r-oldrel: BMTME_1.0.4.zip
OS X binaries: r-release: not available, r-oldrel: not available

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