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: 0.2.1
Depends: R (≥ 3.1.1)
Imports: Rcpp, ggplot2
LinkingTo: Rcpp, RcppArmadillo
Published: 2019-10-30
Author: Sebastian Calonico, Matias D. Cattaneo, Max H. Farrell
Maintainer: Sebastian Calonico <sebastian.calonico at columbia.edu>
License: GPL-2
NeedsCompilation: yes
Citation: nprobust citation info
CRAN checks: nprobust results

Downloads:

Reference manual: nprobust.pdf
Package source: nprobust_0.2.1.tar.gz
Windows binaries: r-devel: nprobust_0.2.1.zip, r-release: nprobust_0.2.1.zip, r-oldrel: nprobust_0.2.1.zip
OS X binaries: r-release: nprobust_0.2.1.tgz, r-oldrel: nprobust_0.2.1.tgz
Old sources: nprobust archive

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