# Tools for Handling Extraction of Features from Time Series

## Documentation for package ‘theft’ version 0.4.1.1

- 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 data quality of a vector
compute_top_features | Return an object containing results from top-performing features on a classification task
demo_multi_outputs | Computed values for multi-feature classification results for use in vignette
demo_outputs | Computed values for top features results for use in vignette
feature_list | All features available in theft in tidy format
fit_multi_feature_classifier | Fit a classifier to feature matrix using all features or all features by set
fit_single_feature_classifier | Fit a classifier to feature matrix to extract top performers
init_theft | Communicate to R the correct Python version containing the relevant libraries for calculating features
minmax_scaler | This function rescales a vector of numerical values into the unit interval [0,1]
normalise_feature_frame | Scale each feature vector into a user-specified range for visualisation and modelling
normalise_feature_vector | Scale each value into a user-specified range for visualisation and analysis
normalize_feature_frame | Scale each feature vector into a user-specified range for visualisation and modelling
normalize_feature_vector | Scale each value into a user-specified range for visualisation and analysis
plot_all_features | Produce a heatmap matrix of the calculated feature value vectors and each unique time series with automatic hierarchical clustering.
plot_feature_correlations | Produce a correlation matrix plot showing pairwise correlations of feature vectors by unique id with automatic hierarchical clustering.
plot_feature_matrix | Produce a heatmap matrix of the calculated feature value vectors and each unique time series with automatic hierarchical clustering.
plot_low_dimension | Produce a principal components analysis (PCA) on normalised feature values and render a bivariate plot to visualise it
plot_quality_matrix | Produce a matrix visualisation of data types computed by feature calculation function.
plot_ts_correlations | Produce a correlation matrix plot showing pairwise correlations of time series with automatic hierarchical clustering
process_hctsa_file | Load in hctsa formatted MATLAB files of time series data into a tidy format ready for feature extraction
robustsigmoid_scaler | This function rescales a vector of numerical values with an outlier-robust Sigmoidal transformation
sigmoid_scaler | This function rescales a vector of numerical values with a Sigmoidal transformation
simData | Sample of randomly-generated time series to produce function tests and vignettes
theft | Tools for Handling Extraction of Features from Time-series
zscore_scaler | This function rescales a vector of numerical values into z-scores