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  "Title": "Fast Local Polynomial Regression and Kernel Density Estimation",
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  "Description": "Non-Uniform Fast Fourier Transform ('NUFFT')-accelerated\nlocal polynomial regression and kernel density estimation for\nlarge, scattered, or complex-valued datasets. Provides\nautomatic bandwidth selection via Generalized Cross-Validation\n(GCV) for regression and Likelihood Cross-Validation (LCV) for\ndensity estimation. This is the 'R' port of the 'fastLPR'\n'MATLAB'/'Python' toolbox, achieving O(N + M log M)\ncomputational complexity through custom 'NUFFT' implementation\nwith Gaussian gridding. Supports 1D/2D/3D data, complex-valued\nresponses, heteroscedastic variance estimation, and confidence\ninterval computation. Performance optimized with vectorized 'R'\ncode and compiled helpers via 'Rcpp'/'RcppArmadillo'. Extends\nthe 'FKreg' toolbox of Wang et al. (2022)\n<doi:10.48550/arXiv.2204.07716> with 'Python' and 'R' ports.\nApplied in Li et al. (2022)\n<doi:10.1016/j.neuroimage.2022.119190>. Uses 'NUFFT' methods\nbased on Greengard and Lee (2004)\n<doi:10.1137/S003614450343200X>, binning-accelerated kernel\nestimation of Wand (1994) <doi:10.1080/10618600.1994.10474656>,\nand local polynomial regression framework of Fan and Gijbels\n(1996, ISBN:978-0412983214).",
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