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FrechForest (version 0.8.1)

Frechet Random Forests

Description

Random forests are a statistical learning method widely used in many areas of scientific research essentially for its ability to learn complex relationships between input and output variables and also its capacity to handle high-dimensional data. However, current random forests approaches are not flexible enough to handle longitudinal heterogeneous data. In this package, we introduce Frchet trees and Frchet random forests, which allow to manage data for which input and output variables are curves. To this end, a new way of splitting the nodes of trees is introduced and the prediction procedures of trees and forests are generalized.

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Version

Install

install.packages('FrechForest')

Monthly Downloads

4

Version

0.8.1

License

GPL-2

Maintainer

Louis Capitaine

Last Published

December 4th, 2019

Functions in FrechForest (0.8.1)

noeuds_deg

Noeud deg
TreeShapeCV

Cross validated Frechet tree
Tmax

Maximal Frechet tree
elagage

Pruning Frechet tree
predict.FrechTree

Frechet Tree prediction
elagageHuberts

Pruning function for Hubert Frechet tree
predict.Frechforest

Frechet random forests prediction function
var_split

Variable split
ordonne

Ordonne time measurements
impurity

Impurity
Frechforest

Frechet random forest
impurity_split

Split Impurity
rf_shape

Frechet random forest
branche

Branche
curve.reduc.times

Title
permutation

Permutation
pred.FT

Frechet Tree prediction
rf_shape_para

Parallelized Frechet random forest
OOB.rfshape

OOB error for a Frechet random forest
FrechForest-package

FrechForest: Frechet Random Forests
ERimpurity_split

Title
Huberts

Hubert's statistic for Frechet tree
DataGenCurves

Curves data generation function
ERvar_split

Title
FrechTree

FrechTree
OOB.tree

OOB.tree
Rtmax

Maximal randomized Fr<U+00E9>chet tree