R: Tools for Handling Extraction of Features from Time Series

# Tools for Handling Extraction of Features from Time Series

## Documentation for package ‘theft’ version 0.8.4

- DESCRIPTION file.
- User guides, package vignettes and other documentation.

## Help Pages

calculate_features | Compute features on an input time series dataset
check_vector_quality | Check for presence of NAs and non-numerics in a vector
feature_list | All features available in theft in tidy format
init_theft | Communicate to R the Python virtual environment containing the relevant libraries for calculating features
init_theft_hctsa | Communicate to R the Python virtual environment containing pyhctsa only
init_theft_kats | Communicate to R the Python virtual environment containing kats only
init_theft_tsfel | Communicate to R the Python virtual environment containing tsfel only
init_theft_tsfresh | Communicate to R the Python virtual environment containing tsfresh only
install_kats | Download and install Kats from Python into a new virtual environment
install_pyhctsa | Download and install pyhctsa from Python into a new virtual environment
install_python_pkgs | Download and install tsfresh, TSFEL, and Kats from Python into a new virtual environment
install_tsfel | Download and install TSFEL from Python into a new virtual environment
install_tsfresh | Download and install tsfresh from Python into a new virtual environment
kurtosis | Calculate a kurtosis of a vector
moments | Calculate a basic set of the four moments of the distribution
process_hctsa_file | Load in hctsa formatted MATLAB files of time series data into a tidy format ready for feature extraction
quantiles | Calculate a basic set of quantiles for an input time-series vector
simData | Sample of randomly-generated time series to produce function tests and vignettes
skewness | Calculate a skewness of a vector
theft | Tools for Handling Extraction of Features from Time-series