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.

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 high-dimensional peaks-over-threshold inference and simulation of Brown-Resnick and extremal Student spatial extremal processes. These include optimization routines based on censored likelihood and gradient scoring, and exact simulation algorithms for max-stable and multivariate Pareto distributions based on rejection sampling. Fast multivariate Gaussian and Student distribution functions using separation-of-variable algorithm with quasi Monte Carlo integration are also provided. Key references include de Fondeville and Davison (2018) <doi:10.1093/biomet/asy026>, Thibaud and Opitz (2015) <doi:10.1093/biomet/asv045>, Wadsworth and Tawn (2014) <doi:10.1093/biomet/ast042> and Genz and Bretz (2009) <doi:10.1007/978-3-642-01689-9>.