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.

Offering enhanced statistical power compared to traditional
    hypothesis testing methods, informative hypothesis testing allows researchers
    to explicitly model their expectations regarding the relationships among
    parameters. An important software tool for this framework is 'restriktor'.
    The 'mmirestriktor' package provides 'shiny' web applications to implement
    some of the basic functionality of 'restriktor'. The mmirestriktor() function
    launches a 'shiny' application for fitting and analyzing models with
    constraints. The FbarCards() function launches a card game application which
    can help build intuition about informative hypothesis testing. The
    iht_interpreter() helps interpret informative hypothesis testing results based
    on guidelines in Vanbrabant and Rosseel (2020) <doi:10.4324/9780429273872-14>.

A set of tools for fitting Markov-modulated linear regression, where responses Y(t) are time-additive, and model operates in the external environment, which is described as a continuous time Markov chain with finite state space.    
    Model is proposed by Alexander Andronov (2012) <arXiv:1901.09600v1> and algorithm of parameters estimation is based on eigenvalues and eigenvectors decomposition.
    Markov-switching regression models have the same idea of varying the regression parameters randomly in accordance with external environment. The difference is that for Markov-modulated linear regression model the external environment is described as a continuous-time homogeneous irreducible Markov chain with known parameters while switching models consider Markov chain as unobserved and estimation procedure involves estimation of transition matrix.
    These models have significant differences in terms of the analytical approach.
    Also, package provides a set of data simulation tools for Markov-modulated linear regression (for academical/research purposes).
    Research project No. 1.1.1.2/VIAA/1/16/075.