BERT                    Adjust data using the BERT algorithm.
adjust_node             Adjust two batches to each other.
adjustment_step         Adjust a hierarchy level sequentially.
chunk_data              Chunks data into n segments with (close-to)
                        equivalent number of batches and stores them in
                        temporary RDS files
compute_asw             Compute the average silhouette width (ASW) for
                        the dataset with respect to both label and
                        batch.
count_existing          Count the number of numeric features in this
                        dataset. Columns labeled "Batch", "Sample" or
                        "Label" will be ignored.
format_DF               Format the data as expected by BERT.
generate_data_covariables
                        Generate dataset with batch-effects and 2
                        classes with a specified imbalance.
generate_dataset        Generate dataset with batch-effects and
                        biological labels using a simple LS model
get_adjustable_features
                        Check, which features contain enough numeric
                        data to be adjusted (at least 2 numeric values)
get_adjustable_features_with_mod
                        Check, which features contain enough numeric
                        data to be adjusted (at least 2 numeric values
                        per batch and covariate level)
identify_adjustableFeatures_refs
                        Identifies the adjustable features using only
                        the references. Similar to the function in
                        adjust_features.R but with different arguments
identify_references     Identifies the references to use for this
                        specific batch effect adjustment
ordinal_encode          Ordinal encoding of a vector.
parallel_bert           Adjusts all chunks of data (in parallel) as far
                        as possible.
removeBatchEffectRefs   A method to remove batch effects estimated from
                        a subset (references) per batch only. Source
                        code is heavily based on
                        limma::removeBatchEffects by Gordon Smyth and
                        Carolyn de Graaf
replace_missing         Replaces missing values (NaN) by NA, this
                        appears to be faster
strip_Covariable        Strip column labelled Cov_1 from dataframe.
validate_bert_input     Verifies that the input to BERT is valid.
validate_input_generate_dataset
                        Validate the user input to the function
                        generate_dataset. Raises an error if and only
                        if the input is malformatted.
verify_references       Verify that the Reference column of the data
                        contains only zeros and ones (if it is present
                        at all)
