Run the same analysis over a range of arbitrary data processing 
    decisions. 'multitool' provides an interface for creating alternative 
    analysis pipelines and turning them into a grid of all possible pipelines. 
    Using this grid as a blueprint, you can model your data across all possible 
    pipelines and summarize the results.

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.)