TemporalForest0.1.4 package

Network-Guided Temporal Forests for Feature Selection in High-Dimensional Longitudinal Data

calculate_fs_metrics_cv

Calculate Feature Selection Metrics

calculate_pred_metrics_cv

Calculate Prediction Metrics

check_temporal_consistency

Check Consistency of Temporal Predictor Data

dot-list.rules.party_safe

Get Split Variable Names from a partykit Tree (Internal)

null_or

Null-safe (coalescing) operator

print.TemporalForest

Print Method for TemporalForest Objects

select_soft_power

Select the best soft-thresholding power for WGCNA

temporal_forest

Temporal Forest for Longitudinal Feature Selection

TemporalTree_time

Core Temporal Forest Algorithm (Internal)

Download source packageRead PDF manual

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>.

  • Maintainer: Sisi Shao
  • License: MIT + file LICENSE
  • Last published: 2025-12-22

Useful links