PDFEstimator-package: Nonparametric Probability Density Estimation and Analysis

In PDFEstimator: Multivariate Nonparametric Probability Density Estimator

Nonparametric Probability Density Estimation and Analysis

Description

This package provides tools for nonparametric density estimation according to the maximum entropy method described in Farmer and Jacobs (2018). PDFEstimator includes functionality for creating a robust data-driven estimate from a data sample requiring minimal user intervention, thus suitable for high-throughput applications.

Additionally, the package includes advanced plotting and visual diagnostics for confidence thresholding and identification of potentially poorly fitted regions of the estimate. These diagnostics are made available to other density estimation methods through a custom conversion utility, allowing for equitable comparison between estimates.

Details

Main function for estimating the density from a data sample:	estimatePDF
Customized plotting function for visual inspection and analysis:	plot
Plotting function for densities with 2 variables:	plot2d
Plotting function for densities with 3 variables:	plot3d
Conversion utility for estimates obtained by other methods:	convertToPDFe
Calculation of boundaries for user-defined confidence levels:	getTarget
Optional background shading outlining expected variance by position:	plotBeta
Utility for additional point approximation for an existing estimate:	approximatePoints

Author(s)

Jenny Farmer, University of North Carolina at Charlotte. [e-mail].

Donald Jacobs, University of North Carolina at Charlotte. [e-mail].

References

Farmer, J. and D. Jacobs (2018). "High throughput nonparametric probability density estimation." PLoS One 13(5): e0196937. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1371/journal.pone.0196937")}.

PDFEstimator documentation built on Aug. 24, 2023, 9:07 a.m.