nprobust: 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.

Version: 0.1.4
Depends: R (≥ 3.1.1)
Imports: Rcpp, ggplot2
LinkingTo: Rcpp, RcppArmadillo
Published: 2019-01-10
Author: Sebastian Calonico, Matias D. Cattaneo, Max H. Farrell
Maintainer: Sebastian Calonico <scalonico at>
License: GPL-2
NeedsCompilation: yes
CRAN checks: nprobust results


Reference manual: nprobust.pdf
Package source: nprobust_0.1.4.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X binaries: r-release: nprobust_0.1.4.tgz, r-oldrel: nprobust_0.1.4.tgz
Old sources: nprobust archive


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