Description: Multivariate hypothesis tests and the confidence intervals. It can be used to test the hypothesizes about mean vector or vectors (one-sample, two independent samples, paired samples), covariance matrix (one or more matrices), and the correlation matrix. Moreover, it can be used for robust Hotelling T^2 test at one sample case in high dimensional data. For this package, we have benefited from the studies Rencher (2003), Nel and Merwe (1986) <DOI: 10.1080/03610928608829342>, Tatlidil (1996), Tsagris (2014), Villasenor Alva and Estrada (2009) <DOI: 10.1080/03610920802474465>.

Imports: stats, rrcov, mvtnorm, robustbase

Description: Package for 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), hierarchical and non-hierarchical cluster analysis, linear regression, multiple factor analysis (MFA) for quantitative, qualitative, frequency (MFACT) and mixed data, projection pursuit (PP), grant tour method and other useful functions for the multivariate analysis.

Description: Package for 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, projection pursuit (PP), grant tour method and other useful functions for the multivariate analysis.

Description: Package for 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.