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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

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.