README.md

In pnnl/qFeature: Extract Features from Continuous or Discrete Time Series

qFeature

An R package for extracting features from continuous or discrete time series. These features can then be used as inputs to multivariate statistical procedures like clustering, dimensionality reduction, and classification. qFeature constructs the features by using moving windows of regression fits for continuous variables and by summarizing the duration and transistion features of discrete variables. This is a high-speed implementation of the feature extraction methods of the Morning Report Algorithms developed by Brett Amidan and Tom Ferryman.

You can learn more about the algorithms of qFeature in the vignette.

Installation instructions

Begin by installing dependencies from CRAN:

install.packages(c("devtools", "plyr", "moments", "foreach", "doParallel", "knitr", "mvbutils"))

The Smisc package (a dependency of qFeature) and qFeature itself contain C code and require compilation. To do this on a Mac, you'll need Xcode on Windows, you'll need to install R tools * on Linux, compilation should take place "automatically"

With the compilation tools in place, you can now install dependencies and the package itself from the PNNL github site:

devtools::install_github("pnnl/Smisc")
devtools::install_github("pnnl/qFeature")

Getting started

The vignette for the qFeature package is the principal resource for understanding what the package does. After installing the package, you can can browse the package and the vignette as follows:

library(qFeature)
browseVignettes("qFeature")

And a list of all the package functions can be found this way:

help(package = qFeature)

And this will provide citation information:

citation("qFeature")

Contributing

We welcome contributions to this package. Please follow these steps when contributing.

pnnl/qFeature documentation built on May 25, 2019, 10:22 a.m.