This package provides tools for data-driven statistical analysis using local polynomial regression (LPR) and kernel density estimation (KDE) methods as described in Calonico, Cattaneo and Farrell (2018): 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).
| Package: | nprobust |
| Type: | Package |
| Version: | 0.4.0 |
| Date: | 2020-08-24 |
| License: | GPL-2 |
Function for LPR estimation and inference: lprobust
Function for LPR bandwidth selection: lpbwselect
Function for KDE estimation and inference: kdrobust
Function for KDE bandwidth selection: kdbwselect
Function for graphical analysis: nprobust.plot
Calonico, S., M. D. Cattaneo, and M. H. Farrell. 2018. On the Effect of Bias Estimation on Coverage Accuracy in Nonparametric Inference. Journal of the American Statistical Association, 113(522): 767-779. doi:10.1080/01621459.2017.1285776.
Calonico, S., M. D. Cattaneo, and M. H. Farrell. 2019. nprobust: Nonparametric Kernel-Based Estimation and Robust Bias-Corrected Inference. Journal of Statistical Software, 91(8): 1-33. http://dx.doi.org/10.18637/jss.v091.i08.