Added targeted FDA benchmark sensitivity utilities with
run_selectboost_sensitivity_study().
Added simulation controls for confounding_strength,
active_region_scale, and local_correlation so
benchmarks can stress the settings where FDA-aware grouping is expected
to help.
Added shipped benchmark artifacts under
inst/extdata/benchmarks/, including feature-level mean
F1 summaries and ranked selectboost_fda()
versus plain SelectBoost settings.
Added a reproducible benchmark script in
tools/run_selectboost_sensitivity_study.R and updated the
benchmark vignette to read the saved study outputs directly.
SelectBoost.FDA 0.4.0
Added minimal examples to the core functions of the package
Added a validation layer with plain_selectboost(),
simulate_fda_scenario(), evaluate_selection(),
benchmark_selection_methods(), and
run_simulation_study().
Added mapped ground-truth utilities so feature-, group-, and
basis-level recovery can be evaluated on transformed FDA designs.
Added a simulation and benchmarks vignette plus release-hardening
metadata for CI and pkgdown workflows.
SelectBoost.FDA 0.3.0
Added a broader selector interface with lasso,
group_lasso, and sparse_group_lasso aliases,
while keeping backend-specific names available.
Added sparse-group lasso support through the SGL
package.
Added overlapping interval groups and region-aware association
structures for FDA grouping.
Added calibration helpers for stability-selection parameters,
interval widths, and SelectBoost c0 grids.
Added method-comparison utilities to run grouped stability
selection, interval stability selection, FDA-SelectBoost, and optional
FDboost workflows on the same fda_design.
Added a formula interface with fda_design_formula(),
fit_stability_formula(), and
fit_selectboost_formula().
SelectBoost.FDA 0.2.0
Added FDA-native preprocessing objects for identity transforms,
scalar standardization, spline-basis expansion, and FPCA.
Added fitted preprocessing workflows with
fit_fda_preprocessor() and
apply_fda_preprocessor() so training and new-data
transforms use the same mapping.
Extended fda_design() to support multiple functional
predictors, scalar covariates, optional fitted preprocessors, and richer
reversible domain metadata.
Standardized fit outputs across stability selection and SelectBoost
with consistent print(), summary(),
selection_map(), plot(), and
selected() behavior.
Added packaged example datasets for end-to-end workflows and updated
the vignettes to start from raw functional inputs.
Expanded test coverage and refreshed package documentation for the
FDA-native core API.
SelectBoost.FDA 0.1.0
Initial package release.
Added grouped stability selection for functional predictors
represented on grids or in basis form.