Implements orbit counting using a fast combinatorial approach.
	Counts orbits of nodes and edges from edge matrix or data frame, or a
	graph object from the graph package.

Functions to perform subspace clustering and classification.

Provides clustering of genes with similar 
  dose response (or time course) profiles. It implements the method 
  described by Lin et al. (2012).

I tend to repeat the same code chunks over and over again. At
    first, this was fine for me and I paid little attention to such redundancies.
    A little later, when I got tired of manually replacing Linux filepaths with
    the referring Windows versions, and vice versa, I started to stuff some very
    frequently used work-steps into functions and, even later, into a proper R
    package. And that's what this package is - a hodgepodge of various R functions
    meant to simplify (my) everyday-life coding work without, at the same time,
    being devoted to a particular scope of application.

Solve first order autocorrelation problems using an iterative method. This procedure estimates both autocorrelation and beta coefficients recursively until we reach the convergence (8th decimal as default). The residuals are computed after estimating Beta using EGLS approach and Rho is estimated using the previous residuals.

Implements ordered beta regression models, which are for modeling continuous variables with upper and lower bounds, such as
   survey sliders, dose-response relationships and indexes. For more information, see
   Kubinec (2022) <doi:10.31235/osf.io/2sx6y>. The package is a front-end to the R package 'brms', which 
   facilitates a range of regression specifications, including hierarchical, dynamic and
   multivariate modeling.

Algorithms for ordinal causal discovery. This package aims to enable users to discover causality for observational ordinal categorical data with greedy and exhaustive search. See Ni, Y., & Mallick, B. (2022) <https://proceedings.mlr.press/v180/ni22a/ni22a.pdf> "Ordinal Causal Discovery. Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence, (UAI 2022), PMLR 180:1530–1540".