Multi Environment Trials Analysis

Performs stability analysis of multi-environment trial data using parametric and non-parametric methods. Parametric methods includes Additive Main Effects and Multiplicative Interaction (AMMI) analysis by Gauch (2013) , Ecovalence by Wricke (1965), Genotype plus Genotype-Environment (GGE) biplot analysis by Yan & Kang (2003) , geometric adaptability index by Mohammadi & Amri (2008) , joint regression analysis by Eberhart & Russel (1966) , genotypic confidence index by Annicchiarico (1992), Murakami & Cruz's (2004) method, power law residuals (POLAR) statistics by Doring et al. (2015) , scale-adjusted coefficient of variation by Doring & Reckling (2018) , stability variance by Shukla (1972) , weighted average of absolute scores by Olivoto et al. (2019a) , and multi-trait stability index by Olivoto et al. (2019b) . Non-parametric methods includes superiority index by Lin & Binns (1988) , nonparametric measures of phenotypic stability by Huehn (1990) <>, TOP third statistic by Fox et al. (1990) . Functions for computing biometrical analysis such as path analysis, canonical correlation, partial correlation, clustering analysis, and tools for inspecting, manipulating, summarizing and plotting typical multi-environment trial data are also provided.


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1.16.0 by Tiago Olivoto, 2 months ago

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Authors: Tiago Olivoto [aut, cre, cph]

Documentation:   PDF Manual  

GPL-3 license

Imports dplyr, GGally, ggforce, ggplot2, ggrepel, lme4, lmerTest, magrittr, mathjaxr, methods, patchwork, purrr, rlang, tibble, tidyr, tidyselect

Suggests DT, knitr, rmarkdown, roxygen2

See at CRAN