Researchers often have expectations about the relations between means
    of different groups or standardized regression coefficients; using informative
    hypothesis testing to incorporate these expectations into the analysis through
    order constraints increases statistical power
    Vanbrabant and Rosseel (2020) <doi:10.4324/9780429273872-14>. Another valuable
    tool, the Bayes factor, can evaluate evidence for multiple hypotheses without
    concerns about multiple testing, and can be used in Bayesian updating
    Hoijtink, Mulder, van Lissa & Gu (2019) <doi:10.1037/met0000201>. The 'bain'
    R package enables informative hypothesis testing using the Bayes factor. The
    'mmibain' package provides 'shiny' web applications based on 'bain'. The
    RepliCrisis() function launches a 'shiny' card game to simulate the evaluation
    of replication studies while the mmibain() function launches a 'shiny'
    application to fit Bayesian informative hypotheses evaluation models from
    'bain'.

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.