aa_getATestResults      Calculates the A-Test scores observed for all
                        sets, for each sample size
aa_graphATestsForSampleSize
                        Produce a plot for each sample size, showing
                        the A-Test scores for each set of that size
aa_graphSampleSizeSummary
                        Plots a comparison of the maximum A-Test score
                        for each sample size
aa_sampleSizeSummary    Determines the median and maximum A-Test score
                        observed for each sample size
aa_summariseReplicateRuns
                        Summarise results in set folder structure into
                        one single CSV file
analysenetwork_structures
                        Analyse each network structure provided as a
                        potential NN structure
atest                   Calculates the A-test score for two
                        distributions
calculate_fold_MSE      Calculate the mean squared error for this fold
                        in k-fold cross validation
calculate_weights_for_ensemble_model
                        Internal function to calculate the weights for
                        all emulators in the ensemble
createAndEvaluateFolds
                        Create and evaluate folds within k-fold cross
                        validation
createTrainingFold      Create training data fold for k-fold cross
                        validation
create_abc_settings_object
                        Creates ensemble-specific parameters for ABC
                        analysis
create_ensemble         Internal function to create the ensemble
create_neural_network   Create neural network emulator, using neuralnet
                        package
createtest_fold         Create test data fold for k-fold cross
                        validation
determine_optimal_neural_network_structure
                        Determine the optimal hidden layer structure
                        from those provided
efast_generate_medians_for_all_parameter_subsets
                        Generates summary file for stochastic
                        simulations stored in multiple files
efast_generate_sample   Generates parameter sets for variance-based
                        eFAST Sensitivity Analysis
efast_generate_sample_netlogo
                        Prepares Netlogo experiment files for a
                        variance-based sensitivity analysis, using
                        eFAST
efast_get_overall_medians
                        Calculates the summary stats for each parameter
                        set (median of any replicates)
efast_graph_Results     Plot the parition of variance in a simulation
                        response for each measure
efast_netlogo_get_overall_medians
                        Deprecated: Use
                        'efast_netlogo_get_overall_medians'
efast_netlogo_run_Analysis
                        Deprecated: Use 'efast_run_Analysis'
efast_process_netlogo_result
                        Analyses Netlogo simulation data for parameter
                        sets generated for eFAST
efast_run_Analysis      Runs the eFAST Analysis for the pre-generated
                        summary file
emulate_efast_sampled_parameters
                        Emulate simulations for a set of eFAST
                        generated parameter values
emulate_lhc_sampled_parameters
                        Emulate simulations for a set of
                        latin-hypercube generated parameter values
emulated_lhc_values     Latin-hypercube value set use to demonstrate
                        emulated sensitivity analysis
emulation_algorithm_settings
                        Initialise machine-learning algorithms settings
                        for emulation creation
emulator_parameter_evolution
                        Evolve parameter sets that meet a desired
                        ensemble outcome
emulator_predictions    Used to generate predictions from an emulator,
                        normalising data if required
ensemble_abc_wrapper    Wrapper to allow EasyABC functions to run using
                        Ensemble
generate_emulators_and_ensemble
                        Generate a set of emulators and combine into an
                        ensemble
generate_ensemble_from_existing_emulations
                        Generate an ensemble from previously created
                        spartan emulation objects
generate_ensemble_training_set
                        Internal function used to combine test set
                        predictions from emulators to form the ensemble
                        training set
generate_requested_emulations
                        Generate emulators for specified machine
                        learning techniques with provided data
graph_Posteriors_All_Parameters
                        Graph posterior distributions generated for all
                        parameters, to PDF file
kfoldCrossValidation    Perform k-fold cross validation for assessing
                        neural network structure performance
lhc_calculatePRCCForMultipleTimepoints
                        Calculates the PRCC for each parameter at each
                        timepoint, storeing PRCC and P-Value in two
                        different files to make the plot function
                        easier
lhc_countSignificantParametersOverTime
                        Count number of significant (p<0.01) parameters
                        over a timecourse
lhc_generateLHCSummary
                        Summarises simulation behaviour for each
                        parameter set, by median of distribution of
                        replicate runs
lhc_generatePRCoEffs    Generate Partial Rank Correlation Coefficients
                        for parameter/response pairs
lhc_generateTimepointFiles
                        Generates spartan-compatible timepoint files if
                        simulation results over time are in one file
lhc_generate_lhc_sample
                        Generates sets of simulation parameters using
                        latin-hypercube sampling
lhc_generate_lhc_sample_netlogo
                        Prepares Netlogo experiment files for a
                        sampling-based sensitivity analysis, using
                        latin-hypercube sampling
lhc_generate_netlogo_PRCoEffs
                        Deprecated. Use 'lhc_generatePRCoEffs' instead
lhc_graphMeasuresForParameterChange
                        Generates parameter/measure plot for each
                        pairing in the analysis
lhc_graphPRCCForMultipleTimepoints
                        Produce a plot of PRCC values obtained at
                        multiple timepoints
lhc_netlogo_graphMeasuresForParameterChange
                        Deprecated. Use
                        'lhc_graphMeasuresForParameterChange' instead
lhc_plotCoEfficients    Plots the PRCC coefficients against each other
                        for ease of comparison
lhc_polarplot           Creates a polar plot for each response, showing
                        PRCC for each parameter
lhc_process_netlogo_result
                        Analyses Netlogo simulations generated for a
                        latin-hypercube based sensitivity analysis
lhc_process_sample_run_subsets
                        Summarises results of runs for parameter sets
                        generated by a latin-hypercube
normaliseATest          Normalises the A-Test such that it is above 0.5
normalise_dataset       Normalise a dataset such that all values are
                        between 0 and 1
nsga2_set_user_params   Initialise analysis specific parameters for
                        NSGA-2
num.decimals            Diagnostic function used to determine number of
                        decimal places
oat_countResponsesOfDesiredValue
                        Counts the number of simulation responses where
                        a output response equals a desired result, for
                        a specified parameter.
oat_csv_result_file_analysis
                        Performs a robustness analysis for supplied
                        simulation data, comparing simulation behaviour
                        at different parameter values
oat_generate_netlogo_behaviour_space_XML
                        Creates a Netlogo compatible behaviour space
                        experiment for robustness analysis
oat_graphATestsForSampleSize
                        Takes each parameter in turn and creates a plot
                        showing A-Test score against parameter value.
oat_parameter_sampling
                        Create parameter samples for robustness (local)
                        analysis
oat_plotResultDistribution
                        For stochastic simulations plots the
                        distribution of results for each parameter
                        value
oat_processParamSubsets
                        Summarises stochastic, repeated, simulations
                        for all robustness parameter sets into a single
                        file.
oat_process_netlogo_result
                        Takes a Netlogo behaviour space file and
                        performs a robustness analysis from that
                        simulation data
partition_dataset       Partition latin-hypercube summary file to
                        training, testing, and validation
perform_aTest_for_all_sim_measures
                        Performs A-Test to compare all simulation
                        measures
plotATestsFromTimepointFiles
                        Plots the A-Tests for all timepoints being
                        examined
plotPRCCSFromTimepointFiles
                        Plots Graphs for Partial Rank Correlation
                        Coefficients Over Time
plot_compare_sim_observed_to_model_prediction
                        Internal function used to create accuracy plots
                        of the emulation against observed data
ploteFASTSiFromTimepointFiles
                        Plot the Si value for all parameters for
                        multiple simulation timepoints
produce_accuracy_plots_all_measures
                        Internal function used to create accuracy plots
                        of the emulation against observed data, for all
                        measures
produce_accuracy_plots_single_measure
                        Internal function used to create accuracy plots
                        of the emulation against observed data
screen_nsga2_parameters
                        Screens NSGA-2 related parameters, guiding
                        which to select for evolving parameter sets
selectSuitableStructure
                        Selects the most suitable neural network
                        structure from the potentials made
set.nsga_sensitivity_params
                        Set parameters for NSGA-2 sensitivity analysis
sim_data_for_emulation
                        Set of parameter and response pairs for
                        training an emulator of a simulation
tutorial_consistency_set
                        Example dataset showing the structure for
                        consistency analysis data
updateErrorForStructure
                        Add the MSE for a newly examined structure to
                        the list of those already seen
use_ensemble_to_generate_predictions
                        Predict simulation responses for a parameter
                        set using an ensemble
visualise_data_distribution
                        Used to diagnose skew in a training dataset
                        before use in emulation
weight_emulator_predictions_by_ensemble
                        Internal function to weight emulator
                        predictions by that calculated for the ensemble
