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, lpbwselect() for local polynomial bandwidth selection, kdrobust() for kernel density point estimation and robust bias-corrected inference, kdbwselect() for kernel density bandwidth selection, and nprobust.plot() for plotting results. The main methodological and numerical features of this package are described in Calonico, Cattaneo and Farrell (2019, ).


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install.packages("nprobust")

0.3.0 by Sebastian Calonico, 3 months ago


Browse source code at https://github.com/cran/nprobust


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