Package: geneticae 1.0.1

geneticae: Statistical Tools for the Analysis of Multi Environment Agronomic Trials

Provides tools for the analysis of multi-environment agronomic trials, with a specific focus on plant breeding experiments. Implements the Additive Main effects and Multiplicative Interaction (AMMI) model (Gauch, 1992, ISBN:9780444892409) and the Site Regression (SREG) model (Cornelius, 1996, <doi:10.1201/9780367802226>). To ensure reliable results even with outliers or missing data, it includes robust versions of AMMI (Rodrigues et al., 2016, <doi:10.1093/bioinformatics/btv533>) and SREG (Angelini et al., 2022, <doi:10.1080/15427528.2022.2051217>). Furthermore, the package offers advanced imputation techniques for multi-environment data, covering classical methodologies (Arciniegas-Alarcón et al., 2014, <doi:10.2478/bile-2014-0006>) and recently published imputation methods for MET data (Angelini et al., 2024, <doi:10.1007/s10681-024-03344-z>).

Authors:Julia Angelini [aut, cre], Marcos Prunello [aut], Gerardo Cervigni [aut]

geneticae_1.0.1.tar.gz
geneticae_1.0.1.zip(r-4.7-any)geneticae_1.0.1.zip(r-4.6-any)geneticae_1.0.1.zip(r-4.5-any)
geneticae_1.0.1.tgz(r-4.6-any)geneticae_1.0.1.tgz(r-4.5-any)
geneticae_1.0.1.tar.gz(r-4.7-any)geneticae_1.0.1.tar.gz(r-4.6-any)
geneticae_1.0.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
geneticae/json (API)

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

Bug tracker:https://github.com/jangelini/geneticae/issues

Pkgdown/docs site:https://jangelini.github.io

Datasets:
  • plrv - Clones from the PLRV population

On CRAN:

Conda:

4.78 score 1 stars 30 scripts 600 downloads 7 exports 149 dependencies

Last updated from:f008699ae3. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK215
source / vignettesOK256
linux-releaseOK182
macos-releaseOK210
macos-oldrelOK298
windows-develOK161
windows-releaseOK161
windows-oldrelOK100
wasm-releaseOK160

Exports:BiplotImputfunEM.SREGimputationrAMMIModelrAMMIPlotrSREGModelrSREGPlot

Dependencies:abindbackportsbase64encBiobaseBiocGenericsbitbit64bootbroombslibcachemcarcarDataclicliprclustercodetoolscolorspacecorpcorcowplotcpp11crayoncrosstalkDEoptimRDerivdigestdoBydoParalleldplyrDTellipseemmeansestimabilityevaluateFactoMineRfarverfastmapflashClustfontawesomeforcatsforeachforecastFormulafracdifffsgenericsggforceggplot2ggrepelglmnetgluegtablehavenhighrhmshtmltoolshtmlwidgetsirlbaisobanditeratorsjomojquerylibjsonliteknitrlabelinglaterlatticelazyevalleapslifecyclelme4lmtestmagrittrMASSMatrixMatrixModelsmemoisemgcvmicemimeminqamissMDAmitmlmodelrmultcompViewmvtnormnlmenloptrnnetnumDerivordinalotelpanpbkrtestpcaMethodspcaPPpillarpkgconfigpolyclipprettyunitsprogresspromisespurrrquantregR6rappdirsrbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreadrreformulasrlangrmarkdownrobustbaserpartrrcovS7sassscalesscatterplot3dshapeshowtextshowtextdbSparseMstringistringrsurvivalsysfontssystemfontstibbletidyrtidyselecttimeDatetinytextweenrtzdbucminfurcautf8vctrsviridisLitevroomwithrxfunyamlzoo

How to use the geneticae package
Getting Started | Introduction | Datasets | Statistical models for multi-environment trials | AMMI model | Site Regression model | Robust Site Regression model | Imputation methods | References | Session Info
Rendered froma-tutorial.Rmdusingknitr::rmarkdown

Last update: 2026-04-20
Started: 2021-09-13

How to use the geneticae Shiny App
Motivation | Small example
Rendered fromb-shiny.Rmdusingknitr::rmarkdown

Last update: 2026-04-16
Started: 2021-09-13