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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: | 0.4.0 |
| Depends: | R (≥ 3.1.1) |
| Imports: | Rcpp, ggplot2 |
| LinkingTo: | Rcpp, RcppArmadillo |
| Published: | 2020-08-26 |
| 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 |
| Reference manual: | nprobust.pdf |
| Package source: | nprobust_0.4.0.tar.gz |
| Windows binaries: | r-devel: nprobust_0.4.0.zip, r-release: nprobust_0.4.0.zip, r-oldrel: nprobust_0.4.0.zip |
| macOS binaries: | r-release (arm64): nprobust_0.4.0.tgz, r-oldrel (arm64): nprobust_0.4.0.tgz, r-release (x86_64): nprobust_0.4.0.tgz, r-oldrel (x86_64): nprobust_0.4.0.tgz |
| Old sources: | nprobust archive |
| Reverse suggests: | tidyhte |
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