This article presents PDFEstimator, an R package for nonparametric probability density estimation and analysis, as both a practical enhancement and alternative to kernel-based estimators. PDFEstimator creates fast, highly accurate, data-driven probability density estimates for continuous random data through an intuitive interface. Excellent results are obtained for a diverse set of data distributions ranging from 10 to \(10^6\) samples when invoked with default parameter definitions in the absence of user directives. Additionally, the package contains methods for assessing the quality of any estimate, including robust plotting functions for detailed visualization and trouble-shooting. Usage of PDFEstimator is illustrated through a variety of examples, including comparisons to several kernel density methods.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2022-037.zip
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For attribution, please cite this work as
Farmer & Jacobs, "PDFEstimator: An R Package for Density Estimation and Analysis", The R Journal, 2022
BibTeX citation
@article{RJ-2022-037,
author = {Farmer, Jenny and Jacobs, Donald},
title = {PDFEstimator: An R Package for Density Estimation and Analysis},
journal = {The R Journal},
year = {2022},
note = {https://doi.org/10.32614/RJ-2022-037},
doi = {10.32614/RJ-2022-037},
volume = {14},
issue = {2},
issn = {2073-4859},
pages = {305-319}
}