greybox: Toolbox for Model Selection and Combinations for the Forecasting Purposes

Implements model selection and combinations via information criteria based on the values of partial correlations. This allows, for example, solving "fat regression" problems, where the number of variables is much larger than the number of observations. This is driven by the research on information criteria, which is well discussed in Burnham & Anderson (2002) <doi:10.1007/b97636>, and currently developed further by Ivan Svetunkov and Yves Sagaert (working paper in progress). Models developed in the package are tailored specifically for forecasting purposes. So as a results there are several methods that allow producing forecasts from these models and visualising them.

Version: 0.2.1
Depends: R (≥ 3.0.2)
Imports: forecast, stats, graphics, utils, lamW
Suggests: smooth, doMC, doParallel, foreach, numDeriv, testthat, rmarkdown, knitr
Published: 2018-05-01
Author: Ivan Svetunkov [aut, cre] (Lecturer at Centre for Marketing Analytics and Forecasting, Lancaster University, UK)
Maintainer: Ivan Svetunkov <ivan at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Materials: README NEWS
CRAN checks: greybox results


Reference manual: greybox.pdf
Vignettes: Greybox main vignette
Greybox - Rolling Origin
Package source: greybox_0.2.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X binaries: r-release: greybox_0.2.1.tgz, r-oldrel: greybox_0.2.0.tgz
Old sources: greybox archive

Reverse dependencies:

Reverse depends: smooth


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