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 |
| 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 [issues need fixing before 2025-04-23] |
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