TSLA
0.1.2Tree-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).
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Health
- OK2026-04-2214 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-04-1813 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
- OK2026-03-1014 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- References docs
- 7%
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Code & Tests
People & History
2 releases. Pick two to compare their code metrics. R releases are shown for context.
- 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
- Installed size
- not tracked yet
- With dependencies
- not tracked yet