Density estimation and inference methods are widely used in empirical work. When the data has compact support, as all empirical applications de facto do, conventional kernel-based density estimators are inapplicable near or at the boundary because of their well known boundary bias. Alternative smoothing methods are available to handle boundary points in density estimation, but they all require additional tuning parameter choices or other typically ad hoc modifications depending on the evaluation point and/or approach considered. This article discusses the R and Stata package lpdensity implementing a novel local polynomial density estimator proposed  in Cattaneo, Jansson  and Ma ( 2019 ), which is boundary adaptive , fully data-driven and automatic, and requires only the choice of one tuning parameter. The methods implemented also cover local polynomial estimation of the cumulative distribution function and density derivatives , as well as several other theoretical and methodological results . In addition to point estimation and graphical procedures, the package offers consistent variance estimators, mean squared error optimal bandwidth selection, and robust bias-corrected inference  . A comparison with several other density estimation packages and functions available in R using a Monte Carlo experiment is provided.
Density estimation and inference methods are widely used in empirical work. When the underlying distribution has compact support, conventional kernel-based density estimators are no longer consistent near or at the boundary because of their well-known boundary bias. Alternative smoothing methods are available to handle boundary points in density estimation, but they all require additional tuning parameter choices or other typically ad hoc modifications depending on the evaluation point and/or approach considered. This article discusses the R and Stata package lpdensity implementing a novel local polynomial density estimator proposed and studied in Cattaneo, Jansson , and Ma ( 2020a,b ), which is boundary adaptive and involves only one tuning parameter. The methods implemented also cover local polynomial estimation of the cumulative distribution function and density derivatives . In addition to point estimation and graphical procedures, the package offers consistent variance estimators, mean squared error optimal bandwidth selection, robust bias-corrected inference , and confidence bands construction, among other features . A comparison with other density estimation packages available in R using a Monte Carlo experiment is provided.
R
data
underlying distribution
D
as all empirical applications de facto do,
R
inapplicable
no longer consistent
R
well known
well-known
A
and studied
A
,
R
2019
2020a,b
R
, fully data-driven and automatic, and requires only the choice of
and involves only
D
, as well as several other theoretical and methodological results
D
and
A
, and confidence bands construction, among other features
D
several
D
and functions