fdars-core

Pure Rust algorithms for Functional Data Analysis (FDA).

Overview

fdars-core provides high-performance implementations of various FDA methods, designed to be used as a library in Rust projects or as the backend for R/Python bindings.

Installation

Add to your Cargo.toml:

[dependencies]
fdars-core = "0.1"

Or install from the repository:

[dependencies]
fdars-core = { git = "https://github.com/sipemu/fdars" }

Features

Functional Data Operations: Mean, centering, derivatives, Lp norms, geometric median
Depth Measures: Fraiman-Muniz, modal, band, modified band, random projection, random Tukey, functional spatial, kernel functional spatial, modified epigraph index
Distance Metrics: Lp distances, Hausdorff, DTW, Fourier-based semimetric, horizontal shift semimetric
Basis Representations: B-splines, Fourier basis, P-splines with GCV/AIC/BIC selection
Clustering: K-means, fuzzy c-means with silhouette and Calinski-Harabasz validation
Smoothing: Nadaraya-Watson, local linear, local polynomial, k-NN
Regression: Functional PCA, PLS, ridge regression
Outlier Detection: LRT-based outlier detection with bootstrap thresholding

Data Layout

Functional data is represented as column-major matrices stored in flat Vec<f64>:

For n observations with m evaluation points: data[i + j * n] gives observation i at point j
2D surfaces (n observations, m1 x m2 grid): stored as n x (m1*m2) matrices

Example

use fdars_core::{fdata, depth, helpers};

// Create sample functional data (3 observations, 10 points each)
let n = 3;
let m = 10;
let data: Vec<f64> = (0..(n * m)).map(|i| (i as f64).sin()).collect();
let argvals: Vec<f64> = (0..m).map(|i| i as f64 / (m - 1) as f64).collect();

// Compute mean function
let mean = fdata::mean_1d(&data, n, m);

// Compute Fraiman-Muniz depth
let depths = depth::fraiman_muniz_1d(&data, &data, n, n, m, true);

Performance

All computationally intensive operations are parallelized using rayon for multi-core performance.

License

MIT

fdars-core 0.2.0