Identify time intervals of differentially abundant metagenomic features in longitudinal studies (Metwally AA, et al., Microbiome, 2018 <doi:10.1186/s40168-018-0402-y>).

The 'MetAlyzer' S4 object provides methods to read and reformat metabolomics data for convenient data handling, statistics and downstream analysis. The resulting format corresponds to input data of the Shiny app 'MetaboExtract' (<https://www.metaboextract.shiny.dkfz.de/MetaboExtract/>).

Combination of either p-values or modified effect sizes from different
    studies to find differentially expressed genes.

Implements several methods to meta-analyze studies that report the 
    sample median of the outcome. When the primary studies are one-group 
    studies, the methods of McGrath et al. (2019) <doi:10.1002/sim.8013> and 
    Ozturk and Balakrishnan (2020) <doi:10.1002/sim.8738> can be applied to 
    estimate the pooled median. In the two-group context, the 
    methods of McGrath et al. (2020a) <doi:10.1002/bimj.201900036> can be 
    applied to estimate the pooled difference of medians across groups. 
    Additionally, a number of methods (e.g., McGrath et al. (2020b) 
    <doi:10.1177/0962280219889080>, Cai et al. (2021) 
    <doi:10.1177/09622802211047348>, and McGrath et al. (2023) 
    <doi:10.1177/09622802221139233>) are implemented to estimate 
    study-specific (difference of) means and their standard errors in order to 
    estimate the pooled (difference of) means. See McGrath et al. (in press) 
    <doi:10.1002/jrsm.1686> for a detailed guide on using the package.

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> .