SelectBoost-Style Variable Selection for Functional Data Analysis R package SelectBoost.FDA version 0.5.0 Implements 'SelectBoost'-style variable selection workflows for functional data analysis. The package provides FDA-native design and preprocessing objects for raw curves, spline-basis expansions, Functional principal component analysis scores, and scalar covariates; grouped stability-selection routines based on repeated subject-level subsampling; multiple selector backends including lasso, group lasso, and sparse-group lasso; FDA-aware grouping functions and calibration helpers for 'SelectBoost'; method-comparison utilities; a formula interface; simulation, benchmarking, and validation helpers with mapped ground truth; targeted sensitivity-study utilities and shipped benchmark summaries for mean 'F1' comparisons between FDA-aware and plain 'SelectBoost' workflows; small example datasets; and an optional adapter to the native stability-selection interface from the 'FDboost' package. Software Depends: R (>= 2.10) Imports: SelectBoost, stats Suggests: FDboost, glmnet, grpreg, knitr, pkgdown, rmarkdown, SGL, stabs, testthat (>= 3.0.0) Frederic Bertrand <frederic.bertrand@lecnam.net> Comprehensive R Archive Network (CRAN) Frederic Bertrand [cre, aut] (ORCID: <https://orcid.org/0000-0002-0837-8281>) GPL-3 2026-04-10 application/tgz https://CRAN.R-project.org/package=SelectBoost.FDA doi:10.32614/CRAN.package.SelectBoost.FDA