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TSLA

Tree-Guided Rare Feature Selection and Logic Aggregation

v0.1.2 · Mar 17, 2025 · GPL-3

Description

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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r-devel-linux-x86_64-fedora-clang OK
r-devel-linux-x86_64-fedora-gcc OK
r-devel-macos-arm64 OK
r-devel-windows-x86_64 OK
r-oldrel-macos-arm64 OK
r-oldrel-macos-x86_64 OK
r-oldrel-windows-x86_64 OK
r-patched-linux-x86_64 OK
r-release-linux-x86_64 OK
r-release-macos-arm64 OK
r-release-macos-x86_64 OK
r-release-windows-x86_64 OK
Check details (14 non-OK)
OK r-devel-linux-x86_64-debian-clang

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OK r-devel-windows-x86_64

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OK r-oldrel-macos-arm64

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OK r-oldrel-windows-x86_64

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OK r-patched-linux-x86_64

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OK r-release-linux-x86_64

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Check History

OK 14 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Mar 9, 2026

Dependency Network

Dependencies Reverse dependencies Matrix Rcpp pROC PRROC ape phytools data.tree TSLA

Version History

new 0.1.2 Mar 9, 2026