{ "_id": "6a101899acfb0bcc41c857f5", "Package": "leaf", "Type": "Package", "Title": "Learning Equations for Automated Function Discovery", "Version": "0.1.0", "Authors@R": "c(\nperson(\"Francisco\", \"Martins\",\nrole = c(\"cre\", \"aut\", \"cph\"),\nemail = \"francisco.martins@tecnico.ulisboa.pt\",\ncomment = c(ORCID = \"https://orcid.org/0009-0007-9941-2994\")),\nperson(\"Pedro\", \"Cardoso\",\nrole = \"aut\",\nemail = \"pmcardoso@ciencias.ulisboa.pt\",\ncomment = c(ORCID = \"https://orcid.org/0000-0001-8119-9960\")),\nperson(\"Manuel\", \"Lopes\",\nrole = \"aut\",\ncomment = c(ORCID = \"https://orcid.org/0000-0002-6238-8974\")),\nperson(\"Vasco\", \"Branco\",\nrole = \"aut\",\nemail = \"vasco.branco@helsinki.fi\",\ncomment = c(ORCID = \"https://orcid.org/0000-0001-7797-3183\")),\nperson(\"INESC-ID\", role = \"fnd\",\ncomment = \"Financed by FCT - PTDC/CCI-COM/5060/2021\"),\nperson(\"intell-sci-comput\", role = \"cph\",\ncomment = \"Copyright holder of RSRM ()\"))", "Maintainer": "Francisco Martins ", "Description": "A unified framework for symbolic regression (SR) and\nmulti-view symbolic regression (MvSR) designed for complex,\nnonlinear systems, with particular applicability to ecological\ndatasets. The package implements a four-stage workflow: data\nsubset generation, functional form discovery, numerical\nparameter optimization, and multi-objective evaluation. It\nprovides a high-level formula-style interface that abstracts\nand extends multiple discovery engines: genetic programming\n(via PySR), Reinforcement Learning with Monte Carlo Tree Search\n(via RSRM), and exhaustive generalized linear model search.\n'leaf' extends these methods by enabling multi-view discovery,\nwhere functional structures are shared across groups while\nparameters are fitted locally, and by supporting the\nenforcement of domain-specific constraints, such as sign\nconsistency across groups. The framework automatically handles\ndata normalization, link functions, and back-transformation,\nensuring that discovered symbolic equations remain\ninterpretable and valid on the original data scale. Implements\nmethods following ongoing work by the authors (2026, in\npreparation).", "URL": "https://github.com/NabiaAI/Leaf", "Note": "Includes modified Python code from the RSRM project\n() under the MIT\nLicense.", "License": "MIT + file LICENSE", "Copyright": "see inst/COPYRIGHTS", "Encoding": "UTF-8", "SystemRequirements": "Conda, Python (>= 3.10)", "RoxygenNote": "7.3.3", "Config/Needs/website": "rmarkdown", "VignetteBuilder": "knitr", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-22 08:46:04 UTC", "User": "root" }, "Config/testthat/edition": "3", "Author": "Francisco Martins [cre, aut, cph] (ORCID:\n), Pedro Cardoso [aut]\n(ORCID: ), Manuel Lopes\n[aut] (ORCID: ), Vasco\nBranco [aut] (ORCID: ),\nINESC-ID [fnd] (Financed by FCT - PTDC/CCI-COM/5060/2021),\nintell-sci-comput [cph] (Copyright holder of RSRM\n())", "Config/pak/sysreqs": "libpng-dev python3", "Repository": "https://1-0-go.r-universe.dev", "Date/Publication": "2026-04-21 22:03:38 UTC", "RemoteUrl": 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