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): 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.

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Package
nprobust
Type
Package
Title
Nonparametric Robust Estimation and Inference Methods using Local Polynomial Regression and Kernel Density Estimation
Version
0.1.4
Date
2019-01-07
Author
Sebastian Calonico , Matias D. Cattaneo , Max H. Farrell
Maintainer
Sebastian Calonico
Description
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.
Depends
R (>= 3.1.1)
License
GPL-2
Imports
Rcpp, ggplot2
LinkingTo
Rcpp, RcppArmadillo
Packaged
2019-01-10 22:46:17 UTC; SCalonico
NeedsCompilation
yes
Repository
CRAN
Date/Publication
2019-01-10 23:30:04 UTC

install.packages('nprobust')

0.1.4

10 days ago

Sebastian Calonico

GPL-2

Depends on

R (>= 3.1.1)

Imports

Rcpp, ggplot2

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