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nprobust: Kernel Density and Local Polynomial Regression Methods

Estimation, inference, bandwidth selection, and graphical procedures for kernel density and local polynomial regression methods, including robust bias-corrected confidence intervals as described in Calonico, Cattaneo and Farrell (2018, <doi:10.1080/01621459.2017.1285776>). The package includes '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 are described in Calonico, Cattaneo and Farrell (2019, <doi:10.18637/jss.v091.i08>).

Version: 1.0.0
Depends: R (≥ 3.6.0)
Imports: ggplot2
Suggests: testthat (≥ 3.0.0), broom, sandwich
Published: 2026-05-19
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>
BugReports: https://github.com/nppackages/nprobust/issues
License: GPL-3
URL: https://github.com/nppackages/nprobust
NeedsCompilation: no
Citation: nprobust citation info
Materials: README
CRAN checks: nprobust results

Documentation:

Reference manual: nprobust.html , nprobust.pdf

Downloads:

Package source: nprobust_1.0.0.tar.gz
Windows binaries: r-devel: nprobust_0.5.0.zip, r-release: nprobust_1.0.0.zip, r-oldrel: nprobust_1.0.0.zip
macOS binaries: r-release (arm64): nprobust_1.0.0.tgz, r-oldrel (arm64): nprobust_1.0.0.tgz, r-release (x86_64): nprobust_1.0.0.tgz, r-oldrel (x86_64): nprobust_1.0.0.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.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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