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: 2026-04-20 21:50:08 UTC; marti
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>))
Repository: CRAN
Date/Publication: 2026-04-21 20:52:15 UTC
Built: R 4.6.1; ; 2026-06-30 04:24:43 UTC; windows
