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.

Multi-omic (or any multi-view) spectral clustering methods often assume the same number of clusters across all datasets. We supply methods for multi-omic spectral clustering when the number of distinct clusters differs among the omics profiles (views).

Provides functions for measuring
    population divergence from genotypic data.

Perform multivariate modeling of evolved traits, with special attention to
 understanding the interplay of the multi-factorial determinants of their origins
 in complex ecological settings (Stephens, 2007 <doi:10.1016/j.tree.2006.12.003>).
 This software primarily concentrates on phylogenetic regression analysis, enabling
 implementation of tree transformation averaging and visualization functionality.
 Functions additionally support information theoretic approaches
 (Grueber, 2011 <doi:10.1111/j.1420-9101.2010.02210.x>;
 Garamszegi, 2011 <doi:10.1007/s00265-010-1028-7>)
 such as  model averaging and selection of phylogenetic models.
 Accessory functions are also implemented for coef standardization (Cade 2015), 
 selection uncertainty, and variable importance (Burnham & Anderson 2000).
 There are other numerous functions for visualizing confounded variables,
 plotting phylogenetic trees, as well as reporting and exporting modeling results.
 Lastly, as challenges to ecology are inherently multifarious, and therefore often
 multi-dataset, this package features several functions to support the identification,
 interpolation, merging, and updating of missing data and outdated nomenclature.