Simulation results detailed in Esarey and Menger (2019) <doi:10.1017/psrm.2017.42>
    demonstrate that cluster adjusted t statistics (CATs) are an effective method
    for correcting standard errors in scenarios with a small number of clusters.
    The 'mmiCATs' package offers a suite of tools for working with CATs. The
    mmiCATs() function initiates a 'shiny' web application, facilitating
    the analysis of data utilizing CATs, as implemented in the cluster.im.glm()
    function from the 'clusterSEs' package. Additionally, the pwr_func_lmer()
    function is designed to simplify the process of conducting simulations to
    compare mixed effects models with CATs models. For educational purposes, the
    CloseCATs() function launches a 'shiny' application card game, aimed at enhancing
    users' understanding of the conditions under which CATs should be preferred
    over random intercept models.

Implements a computational framework to predict microbial community-based 
     metabolic profiles with 'O2PLS' model. It provides procedures of model 
     training and prediction. Paired microbiome and metabolome data are needed 
     for modeling, and the trained model can be applied to predict metabolites 
     of analogous environments using new microbial feature abundances.

Sharing statistical methods or simulation frameworks through 'shiny'
    applications often requires workflows for handling data. To help save and
    display simulation results, the postgresUI() and postgresServer() functions
    in 'mmints' help with persistent data storage using a 'PostgreSQL' database.
    The 'mmints' package also offers data upload functionality through the
    csvUploadUI() and csvUploadServer() functions which allow users to upload
    data, view variables and their types, and edit variable types before fitting
    statistical models within the 'shiny' application. These tools aim to
    enhance efficiency and user interaction in 'shiny' based statistical and
    simulation applications.