CRAN
GeomComb 1.0
(Geometric) Forecast Combination Methods
Released Nov 27, 2016 by Christoph E. Weiss
Dependencies
ForecastCombinations 1.1 forecast 8.2 mtsdi 0.3.5 Matrix 1.2-13 psych 1.8.3.3 ggplot2 2.2.1
Provides eigenvector-based (geometric) forecast combination methods; also includes simple approaches (simple average, median, trimmed and winsorized mean, inverse rank method) and regression-based combination. Tools for data pre-processing are available in order to deal with common problems in forecast combination (missingness, collinearity).
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>GeomComb</artifactId>
<version>1.0-b17</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:GeomComb')
Test Results
This package was last tested against Renjin 0.9.2625 on Apr 9, 2018.
- auto_combine-examples
- comb_BG-examples
- comb_CLS-examples
- comb_EIG1-examples
- comb_EIG2-examples
- comb_EIG3-examples
- comb_EIG4-examples
- comb_InvW-examples
- comb_LAD-examples
- comb_MED-examples
- comb_NG-examples
- comb_OLS-examples
- comb_SA-examples
- comb_TA-examples
- comb_WA-examples
- cs_dispersion-examples
- foreccomb-examples
- plot.foreccomb_res-examples
- summary.foreccomb_res-examples
- testthat