Routines to perform estimation and inference under the multivariate
  t-distribution <doi:10.1007/s10182-022-00468-2>. Currently, the following methodologies 
  are implemented: multivariate mean and covariance estimation, hypothesis testing 
  about equicorrelation and homogeneity of variances, the Wilson-Hilferty transformation, 
  QQ-plots with envelopes and random variate generation.

Multivariate hypothesis tests and confidence intervals...

Functions to run fixed effects or random effects multivariate meta-analysis.

Computes multivariate normal and t probabilities, quantiles, random deviates, 
  and densities. Log-likelihoods for multivariate Gaussian models and Gaussian copulae 
  parameterised by Cholesky factors of covariance or precision matrices are implemented 
  for interval-censored and exact data, or a mix thereof. Score functions for these 
  log-likelihoods are available. A class representing multiple lower triangular matrices 
  and corresponding methods are part of this package.

A function for plotting multivariate time series data.

Defines predict function that transforms output from a Tweedie
    Generalized Linear Mixed Model (using 'glmmTMB'),
    Generalized Additive Model (using 'mgcv'), or
    spatio-temporal Generalized Linear Mixed Model (using package 'tinyVAST'),
    and returns predicted proportions (and standard errors) across a
    grouping variable from an equivalent multivariate-logit Tweedie model.
    These predicted proportions can then be used for standard plotting
    and diagnostics.  See Thorson et al. 2022 <doi:10.1002/ecy.3637>.

Rudimentary functions for sampling and calculating density from the matrix-variate variance-gamma distribution.

Implementation of Matched Wake Analysis (mwa) for studying causal relationships in spatiotemporal event data, introduced by Schutte and Donnay (2014) <doi:10.1016/j.polgeo.2014.03.001>.

Algorithms for solving various Maximum
    Weight Connected Subgraph Problems, including variants with
    budget constraints, cardinality constraints, weighted edges and signals.
    The package represents an R interface to high-efficient solvers based on
    relax-and-cut approach (Álvarez-Miranda E., Sinnl M. (2017) <doi:10.1016/j.cor.2017.05.015>)
    mixed-integer programming (Loboda A., Artyomov M., and Sergushichev A. (2016) <doi:10.1007/978-3-319-43681-4_17>)
    and simulated annealing.