Supervised Classification for Functional Data via Signed Depth R package fdclassify version 0.1.0 Provides a suite of supervised classifiers for functional data based on the concept of signed depth. The core pipeline computes Fraiman-Muniz (FM) functional depth in either its Tukey or Simplicial variant, derives a signed depth by comparing each curve to a reference median curve via the signed distance integral, and feeds the resulting scalar summary into several classifiers: the k-Ranked Nearest Neighbour (k-RNN) rule, a moving-average smoother, a kernel-density Bayes rule, logistic regression on signed depth and distance to the mode, and a generalised additive model (GAM) classifier. Cross-validation routines for tuning the neighbourhood size k and parametric bootstrap confidence intervals are also included. Software Depends: R (>= 4.1.0) Imports: stats, graphics, mgcv, modeest Suggests: testthat (>= 3.0.0), spelling, knitr, rmarkdown Diego Andrés Pérez Ruiz <diego.perezruiz@manchester.ac.uk> Comprehensive R Archive Network (CRAN) Diego Andrés Pérez Ruiz [aut, cre] (ORCID: <https://orcid.org/0000-0002-9969-6065>), Peter Foster [ths] GPL-3 2026-04-23 application/tgz https://CRAN.R-project.org/package=fdclassify doi:10.32614/CRAN.package.fdclassify en-GB