| bushmanplot | Create a stacked area plot that represents the abundance of integration sites over time. | 
| bw | Calculate the bw index | 
| convert_columnwise_relative | Converts a matrix to relative abundances | 
| evaluate_clustering | Evaluate a clustering using the given method | 
| evaluate_clustering_bw | Evaluate a clustering using the bw index | 
| evaluate_clustering_custom | Evaluate a clustering using a custom evaluation function | 
| evaluate_clustering_dunn | Evaluate a clustering using the dunn index | 
| evaluate_clustering_ptbiserial | Evaluate a clustering using the point-biserial index | 
| evaluate_clustering_sdindex | Evaluate a clustering using the SD-index | 
| evaluate_clustering_silhouette | Evaluate a clustering using the silhouette index | 
| filter_at_tp_biggest_n | Filters a matrix of readouts for the n biggest IS at a certain measurement | 
| filter_at_tp_min | Filters a matrix of readouts for IS that have a minimum occurrence in some measurement | 
| filter_combine_measurements | Combines columns that have the same name. The columns are joined additively. | 
| filter_is_names | Shortens the rownames of a readout matrix to the shortest distinct prefix | 
| filter_match | Filters for columns containing a certain substring. | 
| filter_measurement_names | Splits a vector of strings by a given regexp, selects and rearranges the parts and joins them again | 
| filter_names | Filters a vector of names and returns the shortest common prefix. | 
| filter_nr_tp_min | Filters for a minimum number of time points/measurements | 
| filter_zero_columns | Removes columns that only contain 0 or NA. | 
| filter_zero_rows | Removes rows that only contain 0 or NA. | 
| find_best_nr_cluster | Finds the best number of clusters according to silhouette | 
| get_similarity_matrix | Generate a similarity matrix | 
| ggplot_colors | Get the default ggplot color palette or a color palette based on the ggplot palette, but with sub-colors that differ in their luminance | 
| lineplot_split_clone | Show line plots of all integration sites over time, split into facets by their respective clone. | 
| normalize_timecourse | Normalizes a time course using a given mapping from integration sites to clones. | 
| plot.clusterObj | Plots the clustering based on a clustering object | 
| plot.ISSimilarity | Plots the similarity of integration sites | 
| plot.timeseries | Plots time series data, which consists of multiple measurements over time / place (cols) of different clones / integration sites (rows). | 
| plot_rsquare | Plots R^2 of two integration sites | 
| reconstruct | Apply a clustering algorithm to a given time course. | 
| reconstruct_kmedoid | Calculate the k-medoids clustering for a given time course. | 
| reconstruct_recursive | Apply a clustering algorithm recursively to a given time course. | 
| weighted_spring_model | Plot the relationship of integration sites as a graph. |