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, variáveis canônicas, etc.)

PCAmixdata, candisc, biotools,corrplot, crayon, ape

Multivariate Random Forest for Linearly Related Output Features
In Random Forest prediction has been done for single output feature, while linear relation between the output features are not considered. In this package, using linear relation of the output features, a multivariate random forest prediction has been done.

Models Multivariate Cases Using Random Forests
Models and predicts Multiple Features in single random forest considering the linear relation among the features.

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>.

In Random Forest, prediction has been done for single
output feature, while linear relation between the
output features has not been considered in other
packages. In this package, using linear relation of
the output features, a multivariate random forest
prediction has been done, which can predict all the
output features with one Random forest. If there is
high correlation among the features, this MRF
prediction gives better result than the individual
RFs.

Functions to predict one multi-way array (i.e., a tensor) from another multi-way array, using a low-rank CANDECOMP/PARAFAC (CP) factorization and a ridge (L_2) penalty [Lock, EF (2018) <doi:10.1080/10618600.2017.1401544>].  Also includes functions to sample from the Bayesian posterior of a tensor-on-tensor model.

Variable Importance Measures for Multivariate Random Forests
c(person("Sikdar", "Sharmistha", email = "sharmistha.sikdar@tuck.dartmouth.edu", role = "aut"),
person("Hooker", "Giles", email = "ghooker@berkeley.edu", role="aut"),
person("Kadiyali","Vrinda", email = "kadiayali@cornell.edu", role="ctb"),
person("Dogonadze","Nika", email = "nika.dogonadze@toptal.com", role="cre"))
Calculates post-hoc variable importance measures for multivariate random forests.
These are given by split improvement for splits defined by feature j as measured using user-defined (i.e. training or test) examples.
Importance measures can also be calculated on a per-outcome variable basis using the change in predictions for each split. Both measures can be
optionally thresholded to include only splits that produce statistically significant changes as measured by an F-test.
MultivariateRandomForest (>= 1.1.5), MASS (>= 7.3.0)
Sikdar Sharmistha [aut],
Hooker Giles [aut],
Kadiyali Vrinda [ctb],
Dogonadze Nika [cre]