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>.

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 (Kahle
  2013, "Multivariate polynomials in R", The R Journal, 5(1):162), 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.

Generalized Egger tests for detecting publication bias in meta-analysis for diagnostic accuracy test (Noma (2020) <doi:10.1111/biom.13343>, Noma (2022) <doi:10.48550/arXiv.2209.07270>). These publication bias tests are generally more powerful compared with the conventional univariate publication bias tests and can incorporate correlation information between the outcome variables.

Estimates multivariate subgaussian stable densities 
    and probabilities as well as generates random variates using product 
    distribution theory.  A function for estimating the parameters from 
    data to fit a distribution to data is also provided, using the 
    method from Nolan (2013) <doi:10.1007/s00180-013-0396-7>.

Tools for estimating multivariate probit models,
   calculating conditional and unconditional expectations,
   and calculating marginal effects on conditional and unconditional
   expectations.

Provides methods to construct multivariate grids, which can be used
    for multivariate quadrature. This grids can be based on different quadrature
    rules like Newton-Cotes formulas (trapezoidal-, Simpson's- rule, ...) or Gauss
    quadrature (Gauss-Hermite, Gauss-Legendre, ...). For the construction of the
    multidimensional grid the product-rule or the combination- technique can be
    applied.