Package: TSLA 0.1.2
Jianmin Chen
TSLA: Tree-Guided Rare Feature Selection and Logic Aggregation
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>).
Authors:
TSLA_0.1.2.tar.gz
TSLA_0.1.2.tar.gz(r-4.5-noble)TSLA_0.1.2.tar.gz(r-4.4-noble)
TSLA_0.1.2.tgz(r-4.4-emscripten)TSLA_0.1.2.tgz(r-4.3-emscripten)
TSLA.pdf |TSLA.html✨
TSLA/json (API)
| # Install 'TSLA' in R: |
| install.packages('TSLA', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
- ClassificationExample - Synthesic for the classification example
- RegressionExample - Synthesic for the regression example
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 7 days agofrom:58c26c44fd. Checks:3 OK. Indexed: no.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Mar 17 2025 |
| R-4.5-linux-x86_64 | OK | Mar 17 2025 |
| R-4.4-linux-x86_64 | OK | Mar 17 2025 |
Exports:cal2normcoef_TSLAcv.TSLAget_tree_objectgetaggrgetetmatgetperformplot_TSLApredict_cvTSLApredict_TSLATSLA.fit
Dependencies:apecliclusterGenerationcodacodetoolscombinatcpp11data.treeDEoptimdigestdoParallelexpmfastmatchforeachgenericsglueigraphiteratorslatticelifecyclemagrittrmapsMASSMatrixmnormtnlmenumDerivoptimParallelphangornphytoolspkgconfigplyrpROCPRROCquadprogR6RcppRcppArmadillorlangscatterplot3dstringivctrs
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Tree-Guided Rare Feature Selection and Logic Aggregation | TSLA-package |
| Calculate group norms | cal2norm |
| Synthesic for the classification example | ClassificationExample |
| Get coefficients from a fitted TSLA model | coef_TSLA |
| Cross validation for TSLA | cv.TSLA |
| Tree-guided reparameterization | get_tree_object |
| Generate aggregated features | getaggr |
| Tree-guided expansion | getetmat |
| Get performance metrics for classification | getperform |
| Plot aggregated structure | plot_TSLA |
| Prediction from cross validation | predict_cvTSLA |
| Prediction from TSLA with new data | predict_TSLA |
| Synthesic for the regression example | RegressionExample |
| Solve the TSLA optimization problem | TSLA.fit |
