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:
etree_0.1.0.tar.gz
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etree_0.1.0.tgz(r-4.6-any)etree_0.1.0.tgz(r-4.5-any)
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manual.pdf |manual.html✨
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
Last updated from:190143c708. Checks:7 NOTE, 2 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | NOTE | 258 | ||
| source / vignettes | OK | 293 | ||
| linux-release-x86_64 | NOTE | 270 | ||
| macos-release-arm64 | NOTE | 283 | ||
| macos-oldrel-arm64 | NOTE | 273 | ||
| windows-devel | NOTE | 207 | ||
| windows-release | NOTE | 275 | ||
| windows-oldrel | NOTE | 202 | ||
| wasm-release | OK | 153 |
Exports:depthdist_compeforestetreenodeapplynodeidswidth
Dependencies:abindashBHbitopsbootbrainGraphcliclustercodacodetoolscolorspacecpp11data.tabledeSolvedoParallelenergyevaluatefarverfdafda.uscfdsFNNforeachFormulaggplot2gluegraphongslgtablehdrcdehighrigraphinumisobanditeratorskernlabKernSmoothknitrkskSampleslabelinglatticelibcoinlifecyclelocfitmagrittrMASSMatrixmclustmgcvmulticoolmvtnormnetworkNetworkDistancenlmepartykitpcaPPpermutepillarpkgconfigpracmaR6rainbowrbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRCurlRdpackrlangROptSpacerpartRSpectraS7scalesstatnet.commonSuppDistssurvivalTDAtibbleusedistutf8vctrsviridisLitewithrxfunyaml
eforest(): Random Forests With Energy Trees as Base Learners
Rendered frometree-vignette-eforest.Rmdusingknitr::rmarkdownon Jun 02 2026.Last update: 2022-07-12
Started: 2022-07-04
etree: Classification and Regression With Structured and Mixed-Type Data
Rendered frometree-vignette.Rmdusingknitr::rmarkdownon Jun 02 2026.Last update: 2022-07-12
Started: 2022-04-22
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| etree: Classification and Regression With Structured and Mixed-Type Data | etree-package |
| Classification toy dataset | data_cls |
| Regression toy dataset | data_reg |
| Distances | dist_comp |
| Energy Forests | eforest |
| Energy Tree | etree |
| 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 Trees | depth etree-size width |
| Apply functions over nodes | nodeapply nodeapply.etree nodeapply.party nodeapply.partynode |
| Extract node identifiers. | nodeids nodeids.etree nodeids.party nodeids.partynode |
| Visualization of Energy Trees | plot.etree |
| Predictions for Energy Forests | predict.eforest |
| Predictions for Energy Trees | predict.etree |
