Provides a test of multivariate normality of an unknown sample 
    that does not require estimation of the nuisance parameters, the mean and covariance 
    matrix.  Rather, a sequence of transformations removes these nuisance parameters and
    results in a set of sample matrices that are positive definite.  These matrices are 
    uniformly distributed on the space of positive definite matrices in the unit 
    hyper-rectangle if and only if the original data is multivariate normal (Fairweather,
    1973, Doctoral dissertation, University of Washington). The package performs a 
    goodness of fit test of this hypothesis. In addition to the test, functions in the 
    package give visualizations of the support region of positive definite matrices for 
    bivariate samples.

Implements the 'MVOPR' (Multi-View Orthogonal Projection Regression) method for robust 
    variable selection and integration of multi-modality data.

A flexible framework for fitting multivariate
    ordinal regression models with composite likelihood methods. Methodological details are given in Hirk, Hornik, Vana (2020) <doi:10.18637/jss.v093.i04>.

Detection of multivariate outliers using robust estimates of location and scale. The Minimum Covariance Determinant (MCD) estimator is used to calculate robust estimates of the mean vector and covariance matrix. Outliers are determined based on robust Mahalanobis distances using either an unstructured covariance matrix, a principal components structured covariance matrix, or a factor analysis structured covariance matrix. Includes options for specifying the direction of interest for outlier detection for each variable.

Various methods for multivariate outlier detection: arw, a Mahalanobis-type method with an adaptive outlier cutoff value; locout, a method incorporating local neighborhood; pcout, a method for high-dimensional data; mvoutlier.CoDa, a method for compositional data. References are provided in the corresponding help files.

Fast manipulation of symbolic multivariate polynomials
  using the 'Map' class of the Standard Template Library.  The package
  uses print and coercion methods from the 'mpoly' package but
  offers speed improvements.  It is comparable in speed to the 'spray'
  package for sparse arrays, but retains the symbolic benefits of
  'mpoly'.  To cite the package in publications, use Hankin 2022
  <doi:10.48550/ARXIV.2210.15991>.  Uses 'disordR' discipline.