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# Function reference

## All functions

`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

`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