Tools for data-driven statistical analysis using local polynomial regression and kernel density estimation methods as described in Calonico, Cattaneo and Farrell (2018): 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.
|Author||Sebastian Calonico <[email protected]>, Matias D. Cattaneo <[email protected]>, Max H. Farrell <[email protected]>|
|Maintainer||Sebastian Calonico <[email protected]>|
|Package repository||View on CRAN|
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