The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

leaf: Learning Equations for Automated Function Discovery

A unified framework for symbolic regression (SR) and multi-view symbolic regression (MvSR) designed for complex, nonlinear systems, with particular applicability to ecological datasets. The package implements a four-stage workflow: data subset generation, functional form discovery, numerical parameter optimization, and multi-objective evaluation. It provides a high-level formula-style interface that abstracts and extends multiple discovery engines: genetic programming (via PySR), Reinforcement Learning with Monte Carlo Tree Search (via RSRM), and exhaustive generalized linear model search. 'leaf' extends these methods by enabling multi-view discovery, where functional structures are shared across groups while parameters are fitted locally, and by supporting the enforcement of domain-specific constraints, such as sign consistency across groups. The framework automatically handles data normalization, link functions, and back-transformation, ensuring that discovered symbolic equations remain interpretable and valid on the original data scale. Implements methods following ongoing work by the authors (2026, in preparation).

Version: 0.1.0
Imports: R6, utils, reticulate (≥ 1.30), ggplot2, dplyr, rlang, rappdirs, rstudioapi
Suggests: rmarkdown, knitr, testthat (≥ 3.0.0)
Published: 2026-04-21
DOI: 10.32614/CRAN.package.leaf
Author: Francisco Martins ORCID iD [cre, aut, cph], Pedro Cardoso ORCID iD [aut], Manuel Lopes ORCID iD [aut], Vasco Branco ORCID iD [aut], INESC-ID [fnd] (Financed by FCT - PTDC/CCI-COM/5060/2021), intell-sci-comput [cph] (Copyright holder of RSRM (<https://github.com/intell-sci-comput/RSRM>))
Maintainer: Francisco Martins <francisco.martins at tecnico.ulisboa.pt>
License: MIT + file LICENSE
Copyright: see inst/COPYRIGHTS
leaf copyright details
URL: https://github.com/NabiaAI/Leaf
NeedsCompilation: no
SystemRequirements: Conda, Python (>= 3.10)
Materials: README, NEWS
CRAN checks: leaf results

Documentation:

Reference manual: leaf.html , leaf.pdf
Vignettes: Multi-View Symbolic Regression with leaf (source)
Binary classification with leaf (source)
Cross-Validation for Symbolic Regression (source)
Getting Started with leaf (source)
Initialization (source, R code)
Manual Symbolic Regression: Testing Hypotheses (source)
Minimal Example: Quick Start (source)
Introduction to 2D Pareto Fronts (source, R code)
Train-Test Splitting for Symbolic Regression (source)

Downloads:

Package source: leaf_0.1.0.tar.gz
Windows binaries: r-release: leaf_0.1.0.zip, r-oldrel: leaf_0.1.0.zip
macOS binaries: r-release (arm64): leaf_0.1.0.tgz, r-oldrel (arm64): leaf_0.1.0.tgz, r-release (x86_64): leaf_0.1.0.tgz, r-oldrel (x86_64): leaf_0.1.0.tgz

Linking:

Please use the canonical form https://CRAN.R-project.org/package=leaf to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.