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TemporalForest: Network-Guided Temporal Forests for Feature Selection in
High-Dimensional Longitudinal Data
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>.
| Version: |
0.1.4 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
WGCNA, dynamicTreeCut, flashClust, glmertree, partykit, stats, graphics, grDevices |
| Suggests: |
knitr, rmarkdown, igraph, Matrix, MASS |
| Published: |
2025-12-22 |
| DOI: |
10.32614/CRAN.package.TemporalForest |
| Author: |
Sisi Shao  [aut,
cre],
Jason H. Moore
[aut],
Christina M. Ramirez
[aut] |
| Maintainer: |
Sisi Shao <sisishao at g.ucla.edu> |
| BugReports: |
https://github.com/SisiShao/TemporalForest/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://github.com/SisiShao/TemporalForest |
| NeedsCompilation: |
no |
| Language: |
en-US |
| Citation: |
TemporalForest citation info |
| Materials: |
README |
| CRAN checks: |
TemporalForest results |
Documentation:
Downloads:
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https://CRAN.R-project.org/package=TemporalForest
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.