fastlpr 1.0.0

Initial CRAN Release

Features

• Fast local polynomial regression via NUFFT with O(N + M log M) complexity
• Kernel density estimation (KDE) for 1D, 2D, and 3D data
• Local polynomial regression with orders 0 (Nadaraya-Watson), 1 (local linear), and 2 (local quadratic)
• Complex-valued response support
• Heteroscedastic variance estimation
• Automatic bandwidth selection via GCV (regression) and LCV (density estimation)
• 1-SE rule for conservative bandwidth selection
• Confidence interval computation
• OpenMP parallelization via Rcpp/RcppArmadillo (optional)

Main Functions

• cv_fastlpr() - Cross-validated local polynomial regression
• cv_fastkde() - Cross-validated kernel density estimation
• get_hlist() - Generate bandwidth grid
• fastlpr_predict() - Prediction at new data points
• fastlpr_interval() - Confidence interval computation

Notes

• R port of the MATLAB/Python fastLPR toolbox
• Verified against MATLAB reference implementation (MSE < 1e-8)
• Performance: within 8x of MATLAB speed for all test cases