CRAN

kdensity 1.0.0

Kernel Density Estimation with Parametric Starts and Asymmetric Kernels

Released Feb 27, 2018 by Jonas Moss

This package can be loaded by Renjin but 4 out 6 tests failed.

Dependencies

knitr 1.20 EQL 1.0-0 assertthat 0.2.0 rmarkdown 1.10

Handles univariate non-parametric density estimation with parametric starts and asymmetric kernels in a simple and flexible way. Kernel density estimation with parametric starts involves fitting a parametric density to the data before making a correction with kernel density estimation, see Hjort & Glad (1995) . Asymmetric kernels make kernel density estimation more efficient on bounded intervals such as (0, 1) and the positive half-line. Supported asymmetric kernels are the gamma kernel of Chen (2000) , the beta kernel of Chen (1999) , and the copula kernel of Jones & Henderson (2007) . User-supplied kernels, parametric starts, and bandwidths are supported.

Installation

Maven

This package can be included as a dependency from a Java or Scala project by including the following your project's pom.xml file. Read more about embedding Renjin in JVM-based projects.

<dependencies>
  <dependency>
    <groupId>org.renjin.cran</groupId>
    <artifactId>kdensity</artifactId>
    <version>1.0.0-b6</version>
  </dependency>
</dependencies>
<repositories>
  <repository>
    <id>bedatadriven</id>
    <name>bedatadriven public repo</name>
    <url>https://nexus.bedatadriven.com/content/groups/public/</url>
  </repository>
</repositories>

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Renjin CLI

If you're using Renjin from the command line, you load this library by invoking:

library('org.renjin.cran:kdensity')

Test Results

This package was last tested against Renjin 0.9.2689 on Aug 26, 2018.

Source

R

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Release History