Description: 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.)

Depends: candisc, biotools, corrplot, PCAmixdata
Packaged: 2021-07-23 00:44:31 UTC; AlcineiAzevedo

Packaged: 2021-08-05 02:41:20 UTC; AlcineiAzevedo

Imports: PCAmixdata, candisc, biotools,corrplot, crayon, ape
Packaged: 2021-09-03 16:29:31 UTC; AlcineiAzevedo

Packaged: 2021-09-18 23:14:06 UTC; AlcineiAzevedo

Packaged: 2021-09-27 14:39:32 UTC; AlcineiAzevedo

Title: Multivariate Random Forest for Linearly Related Output Features
Description: 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.

Title: Models Multivariate Cases Using Random Forests
Description: Models and predicts Multiple Features in single random forest considering the linear relation among the features.
Imports: Rcpp (>= 0.12.4), bootstrap, stats

Description: 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>.
Packaged: 2017-05-01 00:21:14 UTC; Raziur_Rahman

Description: 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