Machine Learning Forest Simulator R package MLFS version 0.4.3 Climate-sensitive, single-tree forest simulator based on data-driven machine learning. It simulates the main forest processes— radial growth, height growth, mortality, crown recession, regeneration, and harvesting—so users can assess stand development under climate and management scenarios. The height model is described by Skudnik and Jevšenak (2022) <doi:10.1016/j.foreco.2022.120017>, the basal-area increment model by Jevšenak and Skudnik (2021) <doi:10.1016/j.foreco.2020.118601>, and an overview of the MLFS package, workflow, and applications is provided by Jevšenak, Arnič, Krajnc, and Skudnik (2023), Ecological Informatics <doi:10.1016/j.ecoinf.2023.102115>. Software Depends: R (>= 3.4) Imports: brnn (>= 0.6), ranger (>= 0.13.1), reshape2 (>= 1.4.4), pscl (>= 1.5.5), naivebayes (>= 0.9.7), magrittr (>= 1.5), dplyr (>= 0.7.0), tidyr (>= 1.1.3), tidyselect (>= 1.0.0) Jernej Jevsenak <jernej.jevsenak@gmail.com> Comprehensive R Archive Network (CRAN) Jernej Jevsenak [aut, cre, cph] GPL-3 2025-09-01 application/tgz https://CRAN.R-project.org/package=MLFS doi:10.32614/CRAN.package.MLFS