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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.

Installation

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")

License

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

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Install

install.packages('hdrcde')

Monthly Downloads

12,115

Version

3.3

License

GPL-3

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Maintainer

Rob Hyndman

Last Published

December 21st, 2018

Functions in hdrcde (3.3)

plot.hdrconf

Plot HDRs with confidence intervals
hdrbw

Highest Density Region Bandwidth
hdrconf

HDRs with confidence intervals
maxtemp

Daily maximum temperatures in Melbourne, Australia
modalreg

Nonparametric Multimodal Regression
cde.bandwidths

Bandwidth calculation for conditional density estimation
hdr

Highest Density Regions
hdrscatterplot

Scatterplot showing bivariate highest density regions
lane2

Speed-Flow data for Californian Freeway
BoxCox

Box Cox Transformation
cde

Conditional Density Estimation
hdr.2d

Bivariate Highest Density Regions
hdr.cde

Calculate highest density regions continously over some conditioned variable.
plot.cde

Plots conditional densities