Scope | Overall summary | Correlated but not high-dimensional regimes | High-dimensional stress regime | Reproducing the study
Package: SelectBoost.quantile 0.3.1
SelectBoost.quantile: 'SelectBoost'-Style Variable Selection for Quantile Regression
A 'SelectBoost'-inspired workflow for sparse quantile regression. The package builds correlation neighborhoods, perturbs correlated predictors with a directional sampler inspired by the original 'SelectBoost' internals, refits penalized quantile regression models on the perturbed designs, and aggregates variable-selection frequencies across a path of correlation thresholds.
Authors:
SelectBoost.quantile_0.3.1.tar.gz
SelectBoost.quantile_0.3.1.zip(r-4.7-any)SelectBoost.quantile_0.3.1.zip(r-4.6-any)SelectBoost.quantile_0.3.1.zip(r-4.5-any)
SelectBoost.quantile_0.3.1.tgz(r-4.6-any)SelectBoost.quantile_0.3.1.tgz(r-4.5-any)
SelectBoost.quantile_0.3.1.tar.gz(r-4.7-any)SelectBoost.quantile_0.3.1.tar.gz(r-4.6-any)
SelectBoost.quantile_0.3.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
SelectBoost.quantile/json (API)
| # Install 'SelectBoost.quantile' in R: |
| install.packages('SelectBoost.quantile', repos = c('https://fbertran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/fbertran/selectboost.quantile/issues
Pkgdown/docs site:https://fbertran.github.io
Last updated from:d4b7b09ae3. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 120 | ||
| source / vignettes | OK | 177 | ||
| linux-release | OK | 119 | ||
| macos-release | OK | 90 | ||
| macos-oldrel | OK | 73 | ||
| windows-devel | OK | 69 | ||
| windows-release | OK | 56 | ||
| windows-oldrel | OK | 76 | ||
| wasm-release | OK | 89 |
Exports:benchmark_quantile_selectiondefault_quantile_benchmark_scenariosgroup_componentsgroup_neighborsquantile_lasso_selectorselectboost_quantilesimulate_quantile_datasupport_selectboost_quantiletune_lambda_quantile
Dependencies:clueclusterlatticeMASSMatrixMatrixModelsmovMFquantregskmeansslamSparseMsurvivalwithr
Validation Study
Last update: 2026-04-07
Started: 2026-03-29
Getting Started
Overview | Simulate a correlated design | Fit a first model | Summarize and extract stable support | Plot the frequency path | Formula interface and multiple quantiles | Inspect penalty tuning directly | Next steps
Last update: 2026-04-01
Started: 2026-04-01
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Benchmark quantile-selection methods on correlated designs | benchmark_quantile_selection |
| Extract coefficients from a SelectBoost-style quantile fit | coef.selectboost_quantile |
| Default validation scenarios for quantile-selection benchmarks | default_quantile_benchmark_scenarios |
| Grouping functions for SelectBoost.quantile | group_components group_neighbors |
| Plot selection-frequency paths | plot.selectboost_quantile |
| Predict from a SelectBoost-style quantile fit | predict.selectboost_quantile |
| Sparse quantile-regression selector | quantile_lasso_selector |
| SelectBoost-style quantile regression | selectboost_quantile |
| Simulate a sparse quantile-regression problem | simulate_quantile_data |
| Summarize a quantile-selection benchmark | summary.benchmark_quantile_selection |
| Summarize a SelectBoost-style quantile fit | summary.selectboost_quantile |
| Extract selected support at a frequency threshold | support_selectboost_quantile |
| Tune the lasso penalty for sparse quantile regression | tune_lambda_quantile |