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.

Provides a set of basic and extensible data structures and functions for multivariate analysis, including dimensionality reduction techniques, projection methods, and preprocessing functions. The aim of this package is to offer a flexible and user-friendly framework for multivariate analysis that can be easily extended for custom requirements and specific data analysis tasks.

Fully parametric Bayesian multiple imputation framework for massive multivariate data of different variable types as seen in Demirtas, H. (2017) <doi:10.1007/978-981-10-3307-0_8>.