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

Generalized Additive Model for Location, Scale and Shape (GAMLSS) 
    with zero inflated beta (BEZI) family for analysis of microbiome relative abundance data 
    (with various options for data transformation/normalization to address compositional effects) and 
    random effects meta-analysis models for meta-analysis pooling estimates across microbiome studies 
    are implemented. 
    Random Forest model to predict microbiome age based on relative abundances of  
    shared bacterial genera with the Bangladesh data (Subramanian et al 2014), 
    comparison of multiple diversity indexes using linear/linear mixed effect models 
    and some data display/visualization are also implemented.
    The reference paper is published by 
    Ho NT, Li F, Wang S, Kuhn L (2019) <doi:10.1186/s12859-019-2744-2> .

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'.