Man pages for robertness/bninfo
Queries and information theoritic operations on Bayesian networks.
| active_learning | Active Learning Algorithm Orders candidate intervention... |
| add_singletons | Add Single Nodes to a Gaussian Bayesian Network |
| arcs2names | Convert an edge matrix to array of edge names |
| ba_whitelist | DAG Simulation with Preferential Attachment |
| bayes_correction | Bayes Correction |
| bninfo-package | What the package does (short line) ~~ package title ~~ |
| boot_dags | Infer Multiple Structures with Different Starting Graphs |
| break_cycles | Break cycles to produce a DAG Given a set of arcs of a... |
| build_triplet_network | The simplest case of for testing conditional independence is... |
| conditional_entropy_query | Query a Bayesian Network to calculate conditional entropy of... |
| construct_bn | Construct Bayesian network structure directly from arc matrix |
| count_positives | Count the True Positive and False Positive Inferred Edges |
| cpd_entropy_query | Query a Bayesian Network to calculate conditional entropy of... |
| ctsdag | Transition sequence equivalent class |
| dream_net | Dream 4 signaling data |
| edge_entropy | Calculate entropy on model averaging results |
| entropy | Calculate entropy from a probability distribution of a random... |
| fit2net | Convert bn.fit to bn |
| fix_node | Simulate experimental data |
| get_performance | Performance predictions and labels for structure inference... |
| get_upstream_edges | Get upstream edges |
| infer_from_start_net | Apply Structure Inference from a Starting Net |
| info_gain | Calculate information gain |
| l1_error | L1 edge error L1 edge error calculated on a strength object,... |
| melancon_boot | Bootstrap Learning Performed on a Gaussian Bayesian Network. |
| name_edge_df | Convert arc matrix to a data frame with named edges |
| name_edges | Return array of edge names |
| ordered_boot | Bootstrap Learning Performed on a Gaussian Bayesian Network. |
| passive_learning | Passive Learning (Active Learning Algorithm Benchmark)... |
| performance_arc_list | Label arcs accourding to their detection in network inference |
| performance_outcomes | List of true positive, false positive, and false negatives in... |
| performance_plot | Visualizing Performance of Network Inference on Model... |
| progress_plot | Visualize edge detection performance in presence of prior... |
| propagate_orientation | Propagate orientation |
| random_graph | Generate random graphs with a white list of edges |
| rebuild_strength | Covert Dataframe to a Model Averaging Object |
| reduce_averaging | Reduce Averaging |
| sample_size_sim | Simulating the effect of sample size on inference with... |
| scramble | Scramble Labels in a Bayesian Network |
| select_next_intervention | Select a Candidate for Intervention Selects the a target for... |
| select_random_intervention | Randomly Select Candidates for Intervention This function... |
| sim_cond_ind_cor | Simulate Max Absolute Correlation |
| sim_gbn | Fit Gaussian Random Regression Parameters to a Network... |
| sim_median_cor | Simulate Median Correlation |
| skewed_beta | Skewed Edge Priors |
| strength_plot | Visualizing Performance of Network Inference on Model... |
| tcell_examples | Example networks based on T cell data |
| test_dag | Confirm a list of edges forms a DAG. |
| test_markov_dependence_learning | Performance of Causal Inference |
| triplet_network | Gaussian Network Comprised of 3-Node Subnetworks |
| v_array | Convert a DAG to an array of strings representing... |
| zero_gain_prob | Bayesian Hypothesis Test of Zero Information Gain Returns the... |
