Nonparametric Robust Estimation and Inference Methods using Local Polynomial Regression and Kernel Density Estimation

Tools for data-driven statistical analysis using local polynomial regression and kernel density estimation methods as described in Calonico, Cattaneo and Farrell (2018): lprobust() for local polynomial point estimation and robust bias-corrected inference and kdrobust() for kernel density point estimation and robust bias-corrected inference. Several optimal bandwidth selection procedures are computed by lpbwselect() and kdbwselect() for local polynomial and kernel density estimation, respectively. Finally, nprobust.plot() for density and regression plots with robust confidence interval.


Reference manual

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0.1.4 by Sebastian Calonico, 9 months ago

Browse source code at

Authors: Sebastian Calonico <[email protected]> , Matias D. Cattaneo <[email protected]> , Max H. Farrell <[email protected]>

Documentation:   PDF Manual  

GPL-2 license

Imports Rcpp, ggplot2

Linking to Rcpp, RcppArmadillo

See at CRAN