Package: leaf
Type: Package
Title: Learning Equations for Automated Function Discovery
Version: 0.1.0
Authors@R: c(
    person("Francisco", "Martins", 
      role = c("cre", "aut", "cph"),
      email = "francisco.martins@tecnico.ulisboa.pt",
      comment = c(ORCID = "https://orcid.org/0009-0007-9941-2994")),
    person("Pedro", "Cardoso",
      role = "aut",
      email = "pmcardoso@ciencias.ulisboa.pt", 
      comment = c(ORCID = "https://orcid.org/0000-0001-8119-9960")),
    person("Manuel", "Lopes",
      role = "aut",
      comment = c(ORCID = "https://orcid.org/0000-0002-6238-8974")),
    person("Vasco", "Branco",
      role = "aut",
      email = "vasco.branco@helsinki.fi",
      comment = c(ORCID = "https://orcid.org/0000-0001-7797-3183")),
    person("INESC-ID", role = "fnd", 
           comment = "Financed by FCT - PTDC/CCI-COM/5060/2021"),
    person("intell-sci-comput", role = "cph",
      comment = "Copyright holder of RSRM (<https://github.com/intell-sci-comput/RSRM>)"))
Maintainer: Francisco Martins <francisco.martins@tecnico.ulisboa.pt>
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).
URL: https://github.com/NabiaAI/Leaf
Note: Includes modified Python code from the RSRM project
        (<https://github.com/intell-sci-comput/RSRM>) under the MIT
        License.
License: MIT + file LICENSE
Copyright: see inst/COPYRIGHTS
Encoding: UTF-8
Imports: R6, utils, reticulate (>= 1.30), ggplot2, dplyr, rlang,
        rappdirs, rstudioapi
SystemRequirements: Conda, Python (>= 3.10)
RoxygenNote: 7.3.3
Config/Needs/website: rmarkdown
Suggests: rmarkdown, knitr, testthat (>= 3.0.0)
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-10-19 07:49:49 UTC; root
Config/testthat/edition: 3
Author: Francisco Martins [cre, aut, cph] (ORCID:
        <https://orcid.org/0009-0007-9941-2994>), Pedro Cardoso [aut]
        (ORCID: <https://orcid.org/0000-0001-8119-9960>), Manuel Lopes
        [aut] (ORCID: <https://orcid.org/0000-0002-6238-8974>), Vasco
        Branco [aut] (ORCID: <https://orcid.org/0000-0001-7797-3183>),
        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>))
Config/pak/sysreqs: libpng-dev python3
Repository: https://1-0-go.r-universe.dev
Date/Publication: 2026-04-21 22:03:38 UTC
RemoteUrl: https://github.com/cran/leaf
RemoteRef: HEAD
RemoteSha: 88a5398bf4cc9b88dc3a1ab180231c1a97e18133