Changes in version 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