Package: fastlpr
Title: Fast Local Polynomial Regression and Kernel Density Estimation
Version: 1.0.1
Authors@R: c(
    person("Ying", "Wang", email = "yingwangrigel@gmail.com", role = c("aut", "cre")),
    person("Min", "Li", email = "minli.231314@gmail.com", role = "aut")
    )
Description: Non-Uniform Fast Fourier Transform ('NUFFT')-accelerated local polynomial
    regression and kernel density estimation for large, scattered, or
    complex-valued datasets. Provides automatic bandwidth selection via
    Generalized Cross-Validation (GCV) for regression and Likelihood
    Cross-Validation (LCV) for density estimation. This is the 'R' port of the
    'fastLPR' 'MATLAB'/'Python' toolbox, achieving O(N + M log M) computational
    complexity through custom 'NUFFT' implementation with Gaussian gridding.
    Supports 1D/2D/3D data, complex-valued responses, heteroscedastic variance
    estimation, and confidence interval computation. Performance optimized with
    vectorized 'R' code and compiled helpers via 'Rcpp'/'RcppArmadillo'.
    Extends the 'FKreg' toolbox of Wang et al. (2022)
    <doi:10.48550/arXiv.2204.07716> with 'Python' and 'R' ports. Applied in
    Li et al. (2022) <doi:10.1016/j.neuroimage.2022.119190>. Uses 'NUFFT'
    methods based on Greengard and Lee (2004) <doi:10.1137/S003614450343200X>,
    binning-accelerated kernel estimation of Wand (1994)
    <doi:10.1080/10618600.1994.10474656>, and local polynomial regression
    framework of Fan and Gijbels (1996, ISBN:978-0412983214).
License: GPL-3
Encoding: UTF-8
ByteCompile: false
Depends: R (>= 4.2.0)
Imports: stats, utils, grDevices, graphics, compiler, Rcpp (>= 1.0.0)
Suggests: testthat (>= 3.0.0), akima, rgl, R.matlab
LinkingTo: Rcpp, RcppArmadillo
SystemRequirements: GNU make
URL: https://github.com/rigelfalcon/fastLPR
BugReports: https://github.com/rigelfalcon/fastLPR/issues
RoxygenNote: 7.3.1
NeedsCompilation: yes
Packaged: 2026-04-14 01:51:00 UTC; PC
Author: Ying Wang [aut, cre],
  Min Li [aut]
Maintainer: Ying Wang <yingwangrigel@gmail.com>
Repository: CRAN
Date/Publication: 2026-04-21 08:20:02 UTC
