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).