Multimodal mediation analysis is an emerging problem in microbiome data analysis. Multimedia make advanced mediation analysis techniques easy to use, ensuring that all statistical components are transparent and adaptable to specific problem contexts. The package provides a uniform interface to direct and indirect effect estimation, synthetic null hypothesis testing, bootstrap confidence interval construction, and sensitivity analysis. More details are available in Jiang et al. (2024) "multimedia: Multimodal Mediation Analysis of Microbiome Data" <doi:10.1101/2024.03.27.587024>.

Estimates key quantities in causal mediation analysis - including 
    average causal mediation effects (indirect effects), average direct 
    effects, total effects, and proportions mediated - in the presence of 
    multiple uncausally related mediators. Methods are described by 
    Jérolon et al., (2021) <doi:10.1515/ijb-2019-0088> and extended to 
    accommodate survival outcomes as described by Domingo-Relloso et al., 
    (2024) <doi:10.1101/2024.02.16.24302923>.

A set of functions which use the Expectation Maximisation (EM) 
    algorithm (Dempster, A. P., Laird, N. M., and Rubin, D. B. (1977) 
    <doi:10.1111/j.2517-6161.1977.tb01600.x> Maximum likelihood from 
    incomplete data via the EM algorithm, Journal of the Royal Statistical 
    Society, 39(1), 1--22) to take a finite mixture model approach to 
    clustering. The package is designed to cluster multivariate data that have 
    categorical and continuous variables and that possibly contain missing 
    values. The method is described in Hunt, L. and Jorgensen, M. (1999) 
    <doi:10.1111/1467-842X.00071> Australian & New Zealand Journal of Statistics 
    41(2), 153--171 and Hunt, L. and Jorgensen, M. (2003) 
    <doi:10.1016/S0167-9473(02)00190-1> Mixture model clustering for mixed data 
    with missing information, Computational Statistics & Data Analysis, 41(3-4), 
    429--440.

Different examples and methods for testing (including different proposals described in Ameijeiras-Alonso et al., 2019 <DOI:10.1007/s11749-018-0611-5>) and exploring (including the mode tree, mode forest and SiZer) the number of modes using nonparametric techniques <DOI:10.18637/jss.v097.i09>.