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.pdf

Downloads:

Package source: nprobust_0.5.0.tar.gz
Windows binaries: r-devel: nprobust_0.4.0.zip, r-release: nprobust_0.4.0.zip, r-oldrel: nprobust_0.4.0.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:

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