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.

Provides global hypothesis tests, multiple testing procedures and simultaneous confidence intervals for multiple linear contrasts of regression
	coefficients in a single generalized estimating equation (GEE) model or across multiple GEE models. GEE models are fit by a modified version of the 'geeM' package.