Implementation of a procedure for generating samples from a mixed distribution of ordinal and normal random variables with a pre-specified correlation matrix and marginal distributions.
             The details of the method are explained in Demirtas et al. (2015) <DOI:10.1080/10543406.2014.920868>.

Selection, fusion, and/or smoothing of ordinally scaled independent variables using a group lasso, fused lasso or generalized ridge penalty, as well as non-linear principal components analysis for ordinal variables using a second-order difference/smoothing penalty.

Ordination comprises several multivariate exploratory and
    explanatory techniques with theoretical foundations in geometric data
    analysis; see Podani (2000, ISBN:90-5782-067-6) for techniques and
    applications and Le Roux & Rouanet (2005) <doi:10.1007/1-4020-2236-0> for
    foundations.
    Greenacre (2010, ISBN:978-84-923846) shows how the most established of
    these, including principal components analysis, correspondence analysis,
    multidimensional scaling, factor analysis, and discriminant analysis,
    rely on eigen-decompositions or singular value decompositions of
    pre-processed numeric matrix data.
    These decompositions give rise to a set of shared coordinates along which
    the row and column elements can be measured. The overlay of their
    scatterplots on these axes, introduced by Gabriel (1971)
    <doi:10.1093/biomet/58.3.453>, is called a biplot.
    'ordr' provides inspection, extraction, manipulation, and visualization
    tools for several popular ordination classes supported by a set of recovery
    methods. It is inspired by and designed to integrate into 'tidyverse'
    workflows provided by Wickham et al (2019) <doi:10.21105/joss.01686>.

Provides an alternative to R's built-in functionality for handling
    regular expressions, based on the Onigmo library. Offers first-class
    compiled regex objects, partial matching and function-based substitutions,
    amongst other features.