metan: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)
<doi:10.2135/cropsci2013.04.0241>, Ecovalence by Wricke (1965),
Genotype plus Genotype-Environment (GGE) biplot analysis by Yan
& Kang (2003) <doi:10.1201/9781420040371>, geometric
adaptability index by Mohammadi & Amri (2008)
<doi:10.1007/s10681-007-9600-6>, joint regression analysis by
Eberhart & Russel (1966)
<doi:10.2135/cropsci1966.0011183X000600010011x>, genotypic
confidence index by Annicchiarico (1992), Murakami & Cruz's
(2004) method, power law residuals (POLAR) statistics by Doring
et al. (2015) <doi:10.1016/j.fcr.2015.08.005>, scale-adjusted
coefficient of variation by Doring & Reckling (2018)
<doi:10.1016/j.eja.2018.06.007>, stability variance by Shukla
(1972) <doi:10.1038/hdy.1972.87>, weighted average of absolute
scores by Olivoto et al. (2019a)
<doi:10.2134/agronj2019.03.0220>, and multi-trait stability
index by Olivoto et al. (2019b)
<doi:10.2134/agronj2019.03.0221>. Non-parametric methods
includes superiority index by Lin & Binns (1988)
<doi:10.4141/cjps88-018>, nonparametric measures of phenotypic
stability by Huehn (1990) <doi:10.1007/BF00024241>, TOP third
statistic by Fox et al. (1990) <doi:10.1007/BF00040364>.
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.
