fdclassify: Supervised Classification for Functional Data via Signed Depth
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.
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[aut, cre],
Peter Foster [ths]