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... |

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