Tools for data-driven statistical analysis using local polynomial regression and kernel density estimation methods as described in Calonico, Cattaneo and Farrell (2017a): 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: | 0.1.1 |
Imports: | Rcpp, ggplot2 |
LinkingTo: | Rcpp, RcppArmadillo |
Published: | 2017-09-14 |
Author: | Sebastian Calonico, Matias D. Cattaneo, Max H. Farrell |
Maintainer: | Sebastian Calonico <scalonico at bus.miami.edu> |
License: | GPL-2 |
NeedsCompilation: | yes |
CRAN checks: | nprobust results |
Reference manual: | nprobust.pdf |
Package source: | nprobust_0.1.1.tar.gz |
Windows binaries: | r-devel: nprobust_0.1.1.zip, r-release: nprobust_0.1.1.zip, r-oldrel: nprobust_0.1.1.zip |
OS X El Capitan binaries: | r-release: nprobust_0.1.1.tgz |
OS X Mavericks binaries: | r-oldrel: nprobust_0.1.1.tgz |
Old sources: | nprobust archive |
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