| theft-package | Tools for Handling Extraction of Features from Time-series |
| calculate_features | Compute features on an input time series dataset |
| calculate_interval | Calculate interval summaries with a measure of central tendency of classification results |
| check_vector_quality | Check for presence of NAs and non-numerics in a vector |
| compare_features | Conduct statistical testing on time-series feature classification performance to identify top features or compare entire sets |
| feature_list | All features available in theft in tidy format |
| filter_duplicates | Remove duplicate features that exist in multiple feature sets and retain a reproducible random selection of one of them |
| filter_good_features | Filter resample data sets according to good feature list |
| find_good_features | Helper function to find features in both train and test set that are "good" |
| fit_models | Fit classification model and compute key metrics |
| get_rescale_vals | Calculate central tendency and spread values for all numeric columns in a dataset |
| init_theft | Communicate to R the Python virtual environment containing the relevant libraries for calculating features |
| install_python_pkgs | Download and install all the relevant Python packages into a target location |
| make_title | Helper function for converting to title case |
| maxabs_scaler | Rescales a numeric vector using maximum absolute scaling |
| minmax_scaler | Rescales a numeric vector into the unit interval [0,1] |
| normalise | Scale each feature vector into a user-specified range for visualisation and modelling |
| normalize | Scale each feature vector into a user-specified range for visualisation and modelling |
| plot.feature_calculations | Produce a plot for a feature_calculations object |
| plot.low_dimension | Produce a plot for a low_dimension object |
| process_hctsa_file | Load in hctsa formatted MATLAB files of time series data into a tidy format ready for feature extraction |
| reduce_dims | Project a feature matrix into a low dimensional representation using PCA or t-SNE |
| resampled_ttest | Compute correlated t-statistic and p-value for resampled data from correctR package |
| resample_data | Helper function to create a resampled dataset |
| rescale_zscore | Calculate z-score for all columns in a dataset using train set central tendency and spread |
| robustsigmoid_scaler | Rescales a numeric vector using an outlier-robust Sigmoidal transformation |
| select_stat_cols | Helper function to select only the relevant columns for statistical testing |
| sigmoid_scaler | Rescales a numeric vector using a Sigmoidal transformation |
| simData | Sample of randomly-generated time series to produce function tests and vignettes |
| stat_test | Calculate p-values for feature sets or features relative to an empirical null or each other using resampled t-tests |
| theft | Tools for Handling Extraction of Features from Time-series |
| tsfeature_classifier | Fit classifiers using time-series features using a resample-based approach and get a fast understanding of performance |
| zscore_scaler | Rescales a numeric vector into z-scores |