CRAN
tscount 1.4.1
Analysis of Count Time Series
Released Nov 24, 2017 by Tobias Liboschik
Dependencies
Likelihood-based methods for model fitting and assessment, prediction and intervention analysis of count time series following generalized linear models are provided. Models with the identity and with the logarithmic link function are allowed. The conditional distribution can be Poisson or Negative Binomial.
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>tscount</artifactId>
<version>1.4.1-b7</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:tscount')
Test Results
This package was last tested against Renjin 0.9.2644 on Jun 2, 2018.
- QIC-examples
- campy-examples
- ingarch.analytical-examples
- interv_covariate-examples
- interv_detect.tsglm-examples
- interv_test.tsglm-examples
- invertinfo-examples
- marcal-examples
- pit-examples
- plot.tsglm-examples
- predict.tsglm-examples
- residuals.tsglm-examples
- scoring-examples
- se.tsglm-examples
- summary.tsglm-examples
- test_code
- tsglm-examples
- tsglm.sim-examples