Toolbox for Model Selection and Combinations for the Forecasting
Purposes
person("Ivan", "Svetunkov", email = "ivan@svetunkov.ru", role = c("aut", "cre"),
comment="Lecturer at Centre for Marketing Analytics and Forecasting, Lancaster University, UK")
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.
smooth, numDeriv, testthat, rmarkdown, knitr
Ivan Svetunkov [aut, cre] (Lecturer at Centre for Marketing Analytics
and Forecasting, Lancaster University, UK)

smooth, doMC, doParallel, foreach, numDeriv, testthat,
rmarkdown, knitr

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
regressions 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.

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.
forecast, stats, graphics, utils, lamW, numDeriv, nloptr
smooth, doMC, doParallel, foreach, truncreg, testthat,
rmarkdown, knitr