Functions for active causal learning

acl | acl. |

action | Generates the outcome of an intervention or observation of a... |

cat_function | A Cat Function |

choose_int | Get intervention values |

choose_int.nl | Get intervention values |

choose_int.nv | Choose intervention with your own choice of value function |

choose_int.recursive | Get n-step-ahead intervention values |

draw_graph | Uses igraph to draw graphs how I like them |

draw_weighted_graph | Uses igraph to draw weighted graphs |

error_bar | Creates error bars |

generate_unfolding | Unfold a cyclic causal network |

get_joint | Get joint probability distribution over all possible... |

graph_cyclic | Is this graph cyclic |

indi_like | Create likelihoods for a particular observation/intervention... |

initialise_payoffs | Create a value matrix for utility calculations |

likelihood | Create likelihood data.frame |

likelihood_unfolding | Get log likelihood of a particular unfolded graph |

my.graph.formula | A slight mod of graph.formul() from igraphs to avoid the... |

num2bin | Turn numbers to binary |

plotprog | A Cat Function |

posterior_pairwise | Posterior distribution over two graphs given prior and an... |

prior | Generate a prior |

propagation | Propagation function |

renyi_entropy | Computes Renyi entropy |

repmat | Replicates a matrix m by n times |

resave | Resave a rData file with all the previous stuff plus more |

shannon_entropy | Computes Shannon entropy |

similarity_joints | How similar are two joint probability distributions |

tsallis_entropy | Computes Tsallis entropy |

valid_proposals | Valid proposal space for a causal judgement |

virility | Virility property of graphs |

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