Highest Density Regions and Conditional Density Estimation

Computation of highest density regions in one and two dimensions, kernel estimation of univariate density functions conditional on one covariate,and multimodal regression.

hdrcde: Highest Density Regions and Conditional Density Estimation

The R package hdrcde provides tools for computing highest density regions in one and two dimensions, kernel estimates of univariate density functions conditional on one covariate, and multimodal regression.

This package implements the methods described in the following papers.

• Rob J Hyndman (1996) "Computing and graphing highest density regions". American Statistician, 50, 120-126.
• Rob J Hyndman and David Bashtannyk (1996) "Estimating and visualizing conditional densities". Journal of Computational and Graphical Statistics, 5, 315-336.
• David Bashtannyk, Rob J Hyndman (2001) "Bandwidth selection for kernel conditional density estimation". Computational Statistics and Data Analysis 36(3), 279-298.
• Rob J Hyndman and Qiwei Yao (2002) "Nonparametric estimation and symmetry tests for conditional density functions". Journal of Nonparametric Statistics, 14(3), 259-278.
• Einbeck, J., and Tutz, G. (2006). "Modelling beyond regression functions: an application of multimodal regression to speed-flow data". Journal of the Royal Statistical Society, Series C, 55, 461-475.
• Richard J Samworth and Matthew P Wand (2010) "Asymptotics and optimal bandwidth selection for highest density region estimation". The Annals of Statistics, 38, 1767-1792.

Installation

You can install the stable version on R CRAN.

install.packages('hdrcde', dependencies = TRUE)

You can install the development version from Github

pak::pak("robjhyndman/hdrcde")

License

This package is free and open source software, licensed under GPL 3.