A set of tools for displaying, modeling and analysing
        multivariate abundance data in community ecology. See
        'mvabund-package.Rd' for details of overall package organization.
        The package is implemented with the Gnu Scientific Library
        (<http://www.gnu.org/software/gsl/>) and 'Rcpp'
        (<http://dirk.eddelbuettel.com/code/rcpp.html>) 'R' / 'C++' classes.

Calculate Krippendorff's alpha for multi-valued data using the methods 
    introduced by Krippendorff and Craggs (2016) <doi:10.1080/19312458.2016.1228863>.
    Nominal, ordinal, interval, and ratio data types are supported, with option to 
    create bootstrapped estimates of alpha.

Multivariate analysis, having functions that perform simple correspondence analysis (CA) and multiple correspondence analysis (MCA), principal components analysis (PCA), canonical correlation analysis (CCA), factorial analysis (FA), multidimensional scaling (MDS), linear (LDA) and quadratic discriminant analysis (QDA), hierarchical and non-hierarchical cluster analysis, simple and multiple linear regression, multiple factor analysis (MFA) for quantitative, qualitative, frequency (MFACT) and mixed data, biplot, scatter plot, projection pursuit (PP), grant tour method and other useful functions for the multivariate analysis.

Analise multivariada, tendo funcoes que executam analise de correspondencia simples (CA) e multipla (MCA), analise de componentes principais (PCA), analise de correlacao canonica (CCA), analise fatorial (FA), escalonamento multidimensional (MDS), analise discriminante linear (LDA) e quadratica (QDA), analise de cluster hierarquico e nao hierarquico, regressao linear simples e multipla, analise de multiplos fatores (MFA) para dados quantitativos, qualitativos, de frequencia (MFACT) e dados mistos, biplot, scatter plot, projection pursuit (PP), grant tour e outras funcoes uteis para a analise multivariada.

Implements the multivariate autoregressive distributed lag (ARDL) 
    unit root test proposed by Sam, McNown, Goh, and Goh (2024) 
    <doi:10.1080/03796205.2024.2439101>. The test augments the standard ADF 
    regression with lagged levels of a covariate to improve power when 
    cointegration exists. Bootstrap critical values ensure correct size 
    regardless of nuisance parameters. Provides automatic lag selection via 
    AIC/BIC, diagnostic tests, and comprehensive inference tables following 
    the four-case framework.