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.
Author: Rob J Hyndman with contributions from Jochen Einbeck and Matt Wand
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.
You can install the stable version on R CRAN.
install.packages('hdrcde', dependencies = TRUE)
You can install the development version from Github
# install.packages("devtools") devtools::install_github("robjhyndman/hdrcde")
This package is free and open source software, licensed under GPL 3.