Evolutionary Learning of Globally Optimal Trees
Overview
Commonly used classification and regression tree methods like the CART algorithm are recursive partitioning methods that build the model in a forward stepwise search. Although this approach is known to be an efficient heuristic, the results of recursive tree methods are only locally optimal, as splits are chosen to maximize homogeneity at the next step only. An alternative way to search over the parameter space of trees is to use global optimization methods like evolutionary algorithms. The 'evtree' package implements an evolutionary algorithm for learning globally optimal classification and regression trees in R. CPU and memory-intensive tasks are fully computed in C++ while the 'partykit' package is leveraged to represent the resulting trees in R, providing unified infrastructure for summaries, visualizations, and predictions.
- Updated reference output (due to changes in the random sample() generation in 3.6.0)
From NEWS in the release
Install
Health
NOTECRAN incoming feasibilityr-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc
Maintainer: ‘Thomas Grubinger <ThomasGrubinger@gmail.com>’ Package CITATION file contains call(s) to old-style personList() or as.personList(). Please use c() on person objects instead. Package CITATION file contains call(s) to old-style citEntry(). Please use bibentry() instead.
- NOTE11 OK, 2 NOTE2026-06-27
- WARNING10 OK, 2 NOTE, 1 WARNING2026-06-14
- NOTE12 OK, 2 NOTE2026-03-10
Documentation
- Help pages
- 7includes a package overview page
- Examples that run
- 83%5 run in a plain check · 1 never run
- Exported functions with examples
- 50%
- Documented parameters
- 52%
- Return-value docs
- 100%
- Help pages citing references
- 57%
Downloads
Dependencies
Installing evtree adds 8 packages (5 with compiled code, 6.8 MB of source tarballs). Heaviest: partykit 2.1 MB, sandwich 1.4 MB, mvtnorm 1.0 MB.
Code & Tests
Datasets
People & History
Pick two releases to compare their code metrics.
- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- RR 4.3.0 released · 2023-04-21
- RR 4.2.0 released · 2022-04-22
- RR 4.1.0 released · 2021-05-18
- RR 4.0.0 released · 2020-04-24
- 1.0-8Latest
- RR 3.6.0 released · 2019-04-26
- 1.0-72018-05-23 · diff ↗
- RR 3.5.0 released · 2018-04-23
- 1.0-62017-12-15 · diff ↗
- RR 3.4.0 released · 2017-04-21
- 1.0-52017-04-06 · diff ↗
- 1.0-42017-04-02 · diff ↗
- 1.0-32017-03-31 · diff ↗
Show 16 earlier eventsHide 16 earlier events
- 1.0-22017-01-24 · diff ↗
- 1.0-12016-10-14 · diff ↗
- RR 3.3.0 released · 2016-05-03
- RR 3.2.0 released · 2015-04-16
- 1.0-02014-10-15 · diff ↗
- 0.1-52014-07-01 · diff ↗
- RR 3.1.0 released · 2014-04-10
- 0.1-42013-11-26 · diff ↗
- 0.1-32013-06-17 · diff ↗
- RR 3.0.0 released · 2013-04-03
- 0.1-22012-04-11 · diff ↗
- RR 2.15.0 released · 2012-03-30
- RR 2.14.0 released · 2011-10-31
- 0.1-12011-10-02 · diff ↗
- 0.1-02011-09-30
- RR 2.13.0 released · 2011-04-13
Package metadata
- First published
- 2011-09-30
- Total releases
- 15 / 15 yrs
- License
- GPL-2 | GPL-3 OSI
- Minimum R
- ≥ 3.3.0
- Bundled data
- 517 KB / 5 files
- Download size
- 1.3 MB
Cite
Cite this package
Run in R for the authors' entry:
citation("evtree")The package provides a citation file, so citation() returns the authors' own entry.
Cite the R Observatory
For a number measured here: a download total, a coverage figure, an archival date.
From data release v2026-10-18, which the citation names so these numbers can be found later. More on citing and the projects behind them.