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.

robjhyndman/hdrcde documentation built on Sept. 16, 2020, 5:02 a.m.

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