| 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 |