CRAN
GOFSN 1.0
Goodness-of-fit tests for the family of skew-normal models
Released Jul 23, 2012 by Veronica Paton Romero
Dependencies
GOFSN is a package that implements a method for checking if a skew-normal model fits the observed dataset, when all parameters are unknown. While location and scale parameters are estimated by moment estimators, the shape parameter is integrated with respect to the prior predictive distribution, as proposed in (BOX, 1980). A default and proper prior on skewness parameter is used to obtain the prior predictive distribution, as proposed in (CABRAS, CASTELLANOS, 2008). Goodness-of-fit tests, here proposed, depend only on sample size and exhibit full agreement between nominal and actual size. This package implements EDF statistics Kolmogorov-Smirnov(D), Cram\'er-von Mises(W2) and proposes some simple algorithms (SimulD,SimulW2) to approximate their respective marginal predictive distributions. It also has functions (ks.sn,W2.sn) that calculate the p-value on observed data.
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>GOFSN</artifactId>
<version>1.0-b59</version>
</dependency>
</dependencies>
<repositories>
<repository>
<id>bedatadriven</id>
<name>bedatadriven public repo</name>
<url>https://nexus.bedatadriven.com/content/groups/public/</url>
</repository>
</repositories>
Renjin CLI
If you're using Renjin from the command line, you load this library by invoking:
library('org.renjin.cran:GOFSN')
Test Results
This package was last tested against Renjin 0.9.2644 on Jun 2, 2018.