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leaf  

Learning Equations for Automated Function Discovery
View on CRAN: Click here


Download and install leaf package within the R console
Install from CRAN:
install.packages("leaf")

Install from Github:
library("remotes")
install_github("cran/leaf")

Install by package version:
library("remotes")
install_version("leaf", "0.1.0")



Attach the package and use:
library("leaf")
Maintained by
Francisco Martins
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-04-21
Latest Update: 2026-04-21
Description:
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).
How to cite:
Francisco Martins (2026). leaf: Learning Equations for Automated Function Discovery. R package version 0.1.0, https://cran.r-project.org/web/packages/leaf. Accessed 12 Sep. 2026.
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