Network-Guided Temporal Forests for Feature Selection in High-Dimensional Longitudinal Data R package TemporalForest version 0.1.4 Implements the Temporal Forest algorithm for feature selection in high-dimensional longitudinal data. The method combines time-aware network construction via weighted gene co-expression network analysis (WGCNA), module-based feature screening, and stability selection using tree-based models. This package provides tools for reproducible longitudinal analysis, closely following the methodology described in Shao, Moore, and Ramirez (2025) <https://github.com/SisiShao/TemporalForest>. Software Depends: R (>= 3.5.0) Imports: WGCNA, dynamicTreeCut, flashClust, glmertree, partykit, stats, graphics, grDevices, Suggests: knitr, rmarkdown, igraph, Matrix, MASS Sisi Shao <sisishao@g.ucla.edu> Comprehensive R Archive Network (CRAN) Sisi Shao [aut, cre] (ORCID: <https://orcid.org/0009-0000-9783-9205>), Jason H. Moore [aut] (ORCID: <https://orcid.org/0000-0002-5015-1099>), Christina M. Ramirez [aut] (ORCID: <https://orcid.org/0000-0002-8435-0416>) MIT + file LICENSE (https://CRAN.R-project.org/package=TemporalForest/LICENSE) 2025-12-22 application/tgz https://CRAN.R-project.org/package=TemporalForest doi:10.32614/CRAN.package.TemporalForest en-US