Imports: graphics, stats, utils, shiny, ggplot2, gridExtra

with or without factor-by-curve interactions using local polynomial kernel
smoothers or splines. Additionally, a parametric model (allometric model) can be
Imports: graphics, stats, utils, shiny, ggplot2, gridExtra, doParallel,
foreach, mgcv, sfsmisc, shinyjs, wesanderson

Description: A method for obtaining nonparametric estimates of regression models
Imports: graphics, stats, utils, shiny, doParallel, foreach, mgcv,

person("Marta", "Sestelo",email= "sestelo@uvigo.es", role = c("aut", "cre"),

Author: Marta Sestelo [aut, cre] (<https://orcid.org/0000-0003-4284-6509>),

Title: Robust Data-Driven Statistical Inference for Local Polynomial
Regression and Kernel Density Estimation
Author: Sebastian Calonico <scalonico@bus.miami.edu>, Matias D. Cattaneo <cattaneo@umich.edu>, Max H. Farrell <max.farrell@chicagobooth.edu>
Description: Tools for data-driven analytical statistical inference for Local Polynomial Regression estimators and Kernel Density Estimation.
Packaged: 2016-11-07 14:48:14 UTC; SCalonico

Title: Nonparametric Robust Estimation and Inference Methods using
Local Polynomial Regression and Kernel Density Estimation
Description: Tools for data-driven statistical analysis using local polynomial regression and kernel density methods.
Packaged: 2017-08-31 21:31:25 UTC; SCalonico

Description: Tools for data-driven statistical analysis using local polynomial regression and kernel density estimation methods as described in Calonico, Cattaneo and Farrell (2017a): 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.
Packaged: 2017-09-14 19:29:26 UTC; SCalonico

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.