greybox: Toolbox for Model Building and Forecasting

Implements functions and instruments for regression model building and its application to forecasting. The main scope of the package is in variables selection and models specification for cases of time series data. This includes promotional modelling, selection between different dynamic regressions with non-standard distributions of errors, selection based on cross validation, solutions to the fat regression model problem and more. 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: 0.6.2
Depends: R (≥ 3.0.2)
Imports: forecast, stats, graphics, utils, lamW, pracma, nloptr, statmod, zoo, Matrix
LinkingTo: Rcpp
Suggests: smooth (≥ 2.5.1), doMC, doParallel, foreach, testthat, rmarkdown, knitr
Enhances: vars
Published: 2020-09-02
Author: Ivan Svetunkov [aut, cre] (Lecturer at Centre for Marketing Analytics and Forecasting, Lancaster University, UK), Yves R. Sagaert [ctb] (Visiting Research 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: yes
Language: en-GB
Materials: README NEWS
In views: Distributions
CRAN checks: greybox results


Reference manual: greybox.pdf
Vignettes: Advanced Linear Model
Greybox main vignette
Marketing analytics with greybox
Rolling Origin
Package source: greybox_0.6.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release: greybox_0.6.2.tgz, r-oldrel: greybox_0.6.2.tgz
Old sources: greybox archive

Reverse dependencies:

Reverse depends: smooth


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