Tree-Guided Rare Feature Selection and Logic Aggregation
Overview
Implementation of the tree-guided feature selection and logic aggregation approach introduced in Chen et al. (2024) doi:10.1080/01621459.2024.2326621. The method enables the selection and aggregation of large-scale rare binary features with a known hierarchical structure using a convex, linearly-constrained regularized regression framework. The package facilitates the application of this method to both linear regression and binary classification problems by solving the optimization problem via the smoothing proximal gradient descent algorithm (Chen et al. (2012) doi:10.1214/11-AOAS514).
Install
Health
- OK13 OK2026-09-08
- ERROR12 OK, 1 ERROR2026-09-07
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK14 OK2026-04-22
Show 2 earlier changesHide 2 earlier changes
- ERROR13 OK, 1 ERROR2026-04-18
- OK14 OK2026-03-10
Documentation
- Help pages
- 14includes a package overview page
- Examples that run
- 100%3 run in a plain check
- Exported functions with examples
- 27%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 7%
Downloads
Dependencies
Code & Tests
Datasets
People & History
Pick two releases to compare their code metrics.
- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- 0.1.2Latest
- 0.1.12025-02-10
- RR 4.4.0 released · 2024-04-24
Package metadata
- First published
- 2025-02-10
- Total releases
- 2 / 1 yrs
- License
- GPL-3 OSI
- Minimum R
- ≥ 4.1.0
- Bundled data
- 17 KB / 2 files
- Download size
- 49 KB
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