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fastlpr  

Fast Local Polynomial Regression and Kernel Density Estimation
View on CRAN: Click here


Download and install fastlpr package within the R console
Install from CRAN:
install.packages("fastlpr")

Install from Github:
library("remotes")
install_github("cran/fastlpr")

Install by package version:
library("remotes")
install_version("fastlpr", "1.0.1")



Attach the package and use:
library("fastlpr")
Maintained by
Ying Wang
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-04-21
Latest Update: 2026-04-21
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).
How to cite:
Ying Wang (2026). fastlpr: Fast Local Polynomial Regression and Kernel Density Estimation. R package version 1.0.1, https://cran.r-project.org/web/packages/fastlpr. Accessed 24 Oct. 2026.
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