fastlpr: Fast Local Polynomial Regression and Kernel Density Estimation

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).

Version:	1.0.1
Depends:	R (≥ 4.2.0)
Imports:	stats, utils, grDevices, graphics, compiler, Rcpp (≥ 1.0.0)
LinkingTo:	Rcpp, RcppArmadillo
Suggests:	testthat (≥ 3.0.0), akima, rgl, R.matlab
Published:	2026-04-21
DOI:	10.32614/CRAN.package.fastlpr (may not be active yet)
Author:	Ying Wang [aut, cre], Min Li [aut]
Maintainer:	Ying Wang <yingwangrigel at gmail.com>
BugReports:	https://github.com/rigelfalcon/fastLPR/issues
License:	GPL-3
URL:	https://github.com/rigelfalcon/fastLPR
NeedsCompilation:	yes
SystemRequirements:	GNU make
Citation:	fastlpr citation info
Materials:	README, NEWS
CRAN checks:	fastlpr results

Documentation:

Reference manual:

fastlpr.html , fastlpr.pdf

Downloads:

Package source:	fastlpr_1.0.1.tar.gz
Windows binaries:	r-devel: not available, r-release: not available, r-oldrel: not available
macOS binaries:	r-release (arm64): not available, r-oldrel (arm64): not available, r-release (x86_64): not available, r-oldrel (x86_64): not available

Linking:

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