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Experimental Features for Time Series (exts)

The package as it presently stands contains functions experimental and may break at a moments notice. These functions are being developed for use within Fall 2016 courses at the University of Illinois at Urbana-Champaign (UIUC). Specifically, students are using these functions within STAT 578 (Special Topic): Time Series Forecasting and STAT 429: Time Series Analysis. Presently, the functions are meant to provide students with the ability to interact with time series data. All functions will eventually end up either moving to a different package (perhaps gmwm) or may end up being removed. Hence, be wary of applying a dependency to exts as this is serving as a very public skunkworks.

Installation Instructions

Presently, the package can only be obtained through GitHub. Plans for a CRAN-based release are tentative. At some point, this may be added to SMAC's package mirror for the install.

Installing the package through GitHub (Developmental)

For users who are interested in having the latest and greatest developments within time series, this option is ideal. Though, there is considerably more work that a user must do to have a stable version of the package. The setup to obtain the development version is platform dependent.

Specifically, one must have a compiler installed on your system that is compatible with R.

For help on obtaining a compiler consult:

Generally speaking, Linux users should have a compiler that is compatible with R already installed on their system.

With the system dependency taken care of, we continue on by installing the R specific package dependencies and finally the package itself by doing the following in an R session:

# Install dependencies
# install.packages("devtools")

# Install the package from GitHub without Vignettes/User Guides

# Install the package from GitHub with Vignettes/User Guides
# Note: This will be a longer install as the vignettes must be built.
devtools::install_github("SMAC-Group/exts", build_vignettes = TRUE)

Using the package

To use the package simply load it into R via:


Presently, gmwm and magrittr will autoload allowing you to access ts.model, gts, and the pipe (%>%) operator.


Any function or feature you see here is likely to change or be integrated with a different package.


Experimental Features for Time Series




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