apply_NF_simple         Apply NeighborFinder simplest version on raw
                        data
apply_NeighborFinder    Apply NeighborFinder on raw data
choose_params_values    Render a table to give an indication of the
                        values to choose for the prevalence level and
                        the top filtering percentage
compute_precision       Compute precision rate
compute_recall          Compute recall rate
cvglm_to_coeffs_by_object
                        Apply cv.glmnet() for a list of module IDs and
                        for each prevalence level
data                    data
final_step              Gather lists of neighbors of true ones from the
                        graph and detected ones from cv.glmnet()
find_all_module_neighbors
                        Apply cv.glmnet() for a list of module IDs
find_module_neighbors   Apply cv.glmnet() for a given mmodule ID
get_count_table         Conversion to count table function with
                        prevalence filter
graph_step              Generate a graph with a "cluster-like"
                        structure, only needed for simulation purposes
graphs                  graphs
identify_module         List the modules corresponding to a given
                        object of interest
intersections_network   Display the intersection network from 2 or more
                        datasets
intersections_table     Display the intersection table summarizing the
                        results from 2 or more datasets
mclr                    Modified central log ratio (mclr)
                        transformation extracted from the SPRING
                        package
metadata                metadata
module_to_node          Correspondence between the module ID (msp or
                        functional module) and its name (bacteria or
                        function)
new_synth_data          Simulate data from some empirical count dataset
                        with a "cluster-like" structure
norm_data               Normalize data and filters it by prevalence
                        level
prev_for_selected_nodes
                        Extract edges in graph involving any module in
                        object_of_interest set
result_example          result_example
simulate_by_prevalence
                        List the simulated count tables by level of
                        prevalence
simulate_from_ecdf      Simulate data Generates synthetic count data
                        based on empirical cumulative distribution
                        (ecdf) of real count data
taxo                    taxo
test_filter             Render a table gathering precision and recall
                        rates before and after filtering on coefficient
                        values
truth_by_prevalence     Give true neighbors by level of prevalence
visualize_network       Display network after applying NeighborFinder
