Provides utility functions for multivariate analysis (factor analysis, discriminant analysis, and others). The package is primary written for the course Multivariate analysis and for the course Computer intensive methods at the masters program of Applied Statistics at University of Ljubljana.

Functions for simulating, estimating and forecasting stationary Vector Autoregressive (VAR) models for multiple subject data using the penalized multi-VAR framework in Fisher, Kim and Pipiras (2020) <arXiv:2007.05052>.

Distance multivariance is a measure of dependence which can be used to detect 
    and quantify dependence of arbitrarily many random vectors. The necessary functions are
    implemented in this packages and examples are given. It includes: distance multivariance, 
    distance multicorrelation, dependence structure detection, tests of independence and
    copula versions of distance multivariance based on the Monte Carlo empirical transform.
    Detailed references are given in the package description, as starting point for the 
    theoretic background we refer to:
    B. Böttcher, Dependence and Dependence Structures: Estimation and Visualization Using 
    the Unifying Concept of Distance Multivariance. Open Statistics, Vol. 1, No. 1 (2020), 
    <doi:10.1515/stat-2020-0001>.

Package with multivariate analysis methodologies for experiment evaluation.
	 The package estimates dissimilarity measures, builds dendrograms, obtains MANOVA,
	 principal components, canonical variables, etc. (Pacote com metodologias de analise
	 multivariada para avaliação de experimentos. O pacote estima medidas de dissimilaridade,
	 construi de dendogramas, obtem a MANOVA,  componentes principais, variaveis canonicas, etc.)

Models and predicts multiple output features in single random forest considering the 
    linear relation among the output features, see details in Rahman et al (2017)<doi:10.1093/bioinformatics/btw765>.

With foundations on the work by Goutali and Chebana (2024) <doi:10.1016/j.envsoft.2024.106090>, this package contains various 
  univariate and multivariate trend tests. The main functions regard the Multivariate 
  Dependence Trend and Multivariate Overall Trend tests as proposed by Goutali 
  and Chebana (2024), as well as a plotting function that proves useful as a summary 
  and complement of the tests.
  Although many packages and methods carry univariate tests, the Mann-Kendall 
  and Spearman's rho test implementations are included in the package with an 
  adapted version to hydrological formulation (e.g. as in Rao and Hamed 1998 <doi:10.1016/S0022-1694(97)00125-X> or Chebana 2022 <doi:10.1016/C2021-0-01317-1>).
  For better understanding of the example use of the functions, three datasets 
  are included. These are synthetic data and shouldn't be used beyond that purpose.