Creates data with identical statistics (metamers) using an iterative 
   algorithm proposed by Matejka & Fitzmaurice (2017) <DOI:10.1145/3025453.3025912>.

Facilitate frequentist and Bayesian meta-analysis of diagnosis and prognosis research studies. It includes functions to  summarize multiple estimates of prediction model discrimination and calibration performance (Debray et al., 2019) <doi:10.1177/0962280218785504>. It also includes functions to evaluate funnel plot asymmetry (Debray et al., 2018) <doi:10.1002/jrsm.1266>. Finally, the package provides functions for developing multivariable prediction models from datasets with clustering (de Jong et al., 2021) <doi:10.1002/sim.8981>.

Facilitate tasks typically encountered during metabolomics data analysis including data import, filtering, missing value imputation (Stacklies et al. (2007) <doi:10.1093/bioinformatics/btm069>, Stekhoven et al. (2012) <doi:10.1093/bioinformatics/btr597>, Tibshirani et al. (2017) <doi:10.18129/B9.BIOC.IMPUTE>, Troyanskaya et al. (2001) <doi:10.1093/bioinformatics/17.6.520>), normalization (Bolstad et al. (2003) <doi:10.1093/bioinformatics/19.2.185>, Dieterle et al. (2006) <doi:10.1021/ac051632c>, Zhao et al. (2020) <doi:10.1038/s41598-020-72664-6>) transformation, centering and scaling (Van Den Berg et al. (2006) <doi:10.1186/1471-2164-7-142>) as well as statistical tests and plotting. 'metamorphr' introduces a tidy (Wickham et al. (2019) <doi:10.21105/joss.01686>) format for metabolomics data and is designed to make it easier to build elaborate analysis workflows and to integrate them with 'tidyverse' packages including 'dplyr' and 'ggplot2'.

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.