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, <doi:10.1080/01621459.2017.1285776>): '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, <doi:10.18637/jss.v091.i08>).

Version:	0.5.0
Depends:	R (≥ 3.1.1)
Imports:	Rcpp, ggplot2
LinkingTo:	Rcpp, RcppArmadillo
Published:	2025-04-14
DOI:	10.32614/CRAN.package.nprobust
Author:	Sebastian Calonico [aut, cre], Matias D. Cattaneo [aut], Max H. Farrell [aut]
Maintainer:	Sebastian Calonico <scalonico at ucdavis.edu>
License:	GPL-2
NeedsCompilation:	yes
Citation:	nprobust citation info
CRAN checks:	nprobust results

Documentation:

Reference manual:

nprobust.html , nprobust.pdf

Downloads:

Package source:	nprobust_0.5.0.tar.gz
Windows binaries:	r-devel: nprobust_0.5.0.zip, r-release: nprobust_0.5.0.zip, r-oldrel: nprobust_0.5.0.zip
macOS binaries:	r-release (arm64): nprobust_0.5.0.tgz, r-oldrel (arm64): nprobust_0.5.0.tgz, r-release (x86_64): nprobust_0.5.0.tgz, r-oldrel (x86_64): nprobust_0.5.0.tgz
Old sources:	nprobust archive

Reverse dependencies:

Reverse imports:	DIDHAD, rdlearn
Reverse suggests:	tidyhte

Linking:

Please use the canonical form https://CRAN.R-project.org/package=nprobust to link to this page.