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
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
resample_data           Helper function to create a resampled dataset
resampled_ttest         Compute correlated t-statistic and p-value for
                        resampled data from correctR package
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
