Package: etree 0.1.0

etree: Classification and Regression with Structured and Mixed-Type Data

Implementation of Energy Trees, a statistical model to perform classification and regression with structured and mixed-type data. The model has a similar structure to Conditional Trees, but brings in Energy Statistics to test independence between variables that are possibly structured and of different nature. Currently, the package covers functions and graphs as structured covariates. It builds upon 'partykit' to provide functionalities for fitting, printing, plotting, and predicting with Energy Trees. Energy Trees are described in Giubilei et al. (2022) <arxiv:2207.04430>.

Authors:Riccardo Giubilei [aut, cre], Tullia Padellini [aut], Pierpaolo Brutti [aut], Marco Brandi [ctb], Gabriel Nespoli [ctb], Torsten Hothorn [ctb], Achim Zeileis [ctb]

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manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
etree/json (API)

# Install 'etree' in R:
install.packages('etree', repos = c('https://ricgbl.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/ricgbl/etree/issues

Datasets:

On CRAN:

Conda:

4.48 score 3 stars 331 downloads 7 exports 91 dependencies

Last updated from:190143c708. Checks:7 NOTE, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develNOTE272
source / vignettesOK347
linux-releaseNOTE232
macos-releaseNOTE218
macos-oldrelNOTE307
windows-develNOTE274
windows-releaseNOTE186
windows-oldrelNOTE194
wasm-releaseOK175

Exports:depthdist_compeforestetreenodeapplynodeidswidth

Dependencies:abindashBHbitopsbootbrainGraphcliclustercodacodetoolscolorspacecpp11data.tabledeSolvedoParallelenergyevaluatefarverfdafda.uscfdsFNNforeachFormulaggplot2gluegraphongslgtablehdrcdehighrigraphinumisobanditeratorskernlabKernSmoothknitrkskSampleslabelinglatticelibcoinlifecyclelocfitmagrittrMASSMatrixmclustmgcvmulticoolmvtnormnetworkNetworkDistancenlmepartykitpcaPPpermutepillarpkgconfigpracmaR6rainbowrbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRCurlRdpackrlangROptSpacerpartRSpectraS7sandwichscalesstatnet.commonstrucchangeSuppDistssurvivalTDAtibbleusedistutf8vctrsviridisLitewithrxfunyamlzoo

eforest(): Random Forests With Energy Trees as Base Learners
Introduction | Data generation | Function eforest() | Regression | Classification | References
Rendered frometree-vignette-eforest.Rmdusingknitr::rmarkdown

Last update: 2022-07-12
Started: 2022-07-04

etree: Classification and Regression With Structured and Mixed-Type Data
Introduction | Data generation | Regression | Quick fit | Changing parameters | Print and other utilities | Predict | Classification | References
Rendered frometree-vignette.Rmdusingknitr::rmarkdown

Last update: 2022-07-12
Started: 2022-04-22

Readme and manuals

Help Manual

Help pageTopics
etree: Classification and Regression With Structured and Mixed-Type Dataetree-package
Classification toy datasetdata_cls
Regression toy datasetdata_reg
Distancesdist_comp
Energy Forestseforest
Energy Treeetree
Methods for "etree" objects"[.etree" "[[.etree" depth.etree depth.party etree-methods length.etree length.party print.etree width.etree width.party [.etree [[.etree
Size of Energy Treesdepth etree-size width
Apply functions over nodesnodeapply nodeapply.etree nodeapply.party nodeapply.partynode
Extract node identifiers.nodeids nodeids.etree nodeids.party nodeids.partynode
Visualization of Energy Treesplot.etree
Predictions for Energy Forestspredict.eforest
Predictions for Energy Treespredict.etree