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 (2017a): 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.


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install.packages("nprobust")

0.1.1 by Sebastian Calonico, 6 days ago


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


Authors: Sebastian Calonico <scalonico@bus.miami.edu>, Matias D. Cattaneo <cattaneo@umich.edu>, Max H. Farrell <max.farrell@chicagobooth.edu>


Documentation:   PDF Manual  


GPL-2 license


Imports Rcpp, ggplot2

Linking to Rcpp, RcppArmadillo


See at CRAN