fdars 0.3.2

Bug Fixes

• Fixed compiled code WARNING: wrapped abort/exit/_exit symbols using linker --wrap flag to convert process termination into R errors
• Reduced test CPU time by limiting Rust thread pool (RAYON_NUM_THREADS=2)

fdars 0.3.1

Bug Fixes

• Fixed Windows installation failure (missing cargo checksum files)
• Wrapped slow bootstrap examples in \donttest{}

fdars 0.3.0

Internal

• Upgraded Rust backend (fdars-core) to v0.4.0
• New FdMatrix type for safer matrix handling (internal)
• New streaming depth module in core (internal)
• Reduced package size by removing non-essential vendored files
• No user-facing API changes

fdars 0.2.0

Test Coverage & Quality

• Improved Rust core test coverage to 84%+
• Improved R package test coverage to 80%+
• Added pre-commit hooks for cargo fmt and clippy

New Features

Optimal Cluster Selection

• Added optim.kmeans.fd() function to automatically determine the optimal number of clusters for functional k-means
• Three selection criteria available:
  • Silhouette score: Measures cluster cohesion vs separation (-1 to 1, higher is better)
  • Calinski-Harabasz index: Ratio of between/within cluster variance (higher is better)
  • Elbow method: Visual inspection of within-cluster sum of squares
• Added print() and plot() methods for optim.kmeans.fd objects
• Silhouette and Calinski-Harabasz computations implemented in Rust for performance

k-NN Bandwidth Selection for Nonparametric Regression

• Added k-nearest neighbors support to fregre.np() via the type.S parameter:
  • "kNN.gCV": Global cross-validation (single k for all observations)
  • "kNN.lCV": Local cross-validation (adaptive k per observation)
• Extended predict.fregre.np() to handle k-NN models

Flexible Metrics in Clustering

• kmeans.fd() now accepts both string metrics ("L2", "L1", "Linf") and metric/semimetric functions
• String metrics use fast Rust-only path; function metrics provide flexibility for custom distances

Improvements

ggplot2 Visualizations

• All plot methods now use ggplot2 instead of base R graphics:
  • plot.fdata(): Functional data curves with minimal theme
  • plot.kmeans.fd(): Cluster-colored curves with dashed cluster centers
  • plot.optim.kmeans.fd(): Criterion scores with optimal k highlighted
  • plot.outliers.fdata(): Outlier/normal curves with color legend

Documentation

Vignettes

Added 6 comprehensive vignettes: - Introduction to fdars - Functional Depth Functions - Distance Metrics and Semimetrics - Functional Regression - Functional Clustering - Outlier Detection

API Documentation

• Complete roxygen2 documentation for all exported functions

Bug Fixes

• Fixed namespace issues with stats and utils imports
• Fixed ggplot2 .data pronoun import for R CMD check compliance

fdars 0.1.0

• Initial release
• Core functional data class (fdata) with 1D and 2D support
• 7 depth functions: FM, mode, RP, RT, FSD, KFSD, RPD
• Distance metrics: Lp, Hausdorff, DTW, KL
• Semimetrics: PCA, derivative, basis, Fourier, hshift
• Functional regression: PC, basis, nonparametric
• K-means clustering with k-means++ initialization
• Outlier detection: depth-based and LRT methods
• Statistical tests: flm.test, fmean.test
• Bootstrap inference and confidence intervals
• High-performance Rust backend with parallel processing