acc_successions | Returns a vector with the number of consecutive nodes in each level |

add_attr_to_fit | Adds the mu vector and sigma matrix as attributes to the bn.fit or dbn.fit object |

approximate_inference | Performs approximate inference forecasting with the GDBN over a data set |

approx_prediction_step | Performs approximate inference in a time slice of the dbn |

calc_mu | Calculate the mu vector of means of a Gaussian linear network. Front end of a C++ function. |

calc_mu_cpp | Calculate the mu vector of means of a Gaussian linear network. This is the C++ backend of the function. |

calc_sigma | Calculate the sigma covariance matrix of a Gaussian linear network. Front end of a C++ function. |

calc_sigma_cpp | Calculate the sigma covariance matrix of a Gaussian linear network. This is the C++ backend of the function. |

Causlist | This file contains all the classes needed for the PSOHO structure learning algorithm. It was implemented as an independent package in https://github.com/dkesada/PSOHO and then merged into dbnR. All the original source files are merged into one to avoid bloating the R/ folder of the package. |

check_time0_formatted | Checks if the vector of names are time formatted to t0 |

cl_to_arc_matrix_cpp | Create a matrix with the arcs defined in a causlist object |

create_blacklist | Creates the blacklist of arcs from a folded data.table |

create_causlist_cpp | Create a causal list from a DBN. This is the C++ backend of the function. |

cte_times_vel_cpp | Multiply a Velocity by a constant real number |

dmmhc | Learns the structure of a markovian n DBN model from data |

dynamic_ordering | Gets the ordering of a single time slice in a DBN |

exact_inference | Performs exact inference forecasting with the GDBN over a data set |

exact_prediction_step | Performs exact inference in a time slice of the dbn |

expand_time_nodes | Extends the names of the nodes in t_0 to t_(max-1) |

fit_dbn_params | Fits a markovian n DBN model |

fold_dt | Widens the dataset to take into account the t previous time slices |

fold_dt_rec | Widens the dataset to take into account the t previous time slices |

forecast_ts | Performs forecasting with the GDBN over a data set |

initialize_cl_cpp | Create a causality list and initialize it |

init_list_cpp | Initialize the particles |

learn_dbn_struc | Learns the structure of a markovian n DBN model from data |

merge_nets | Merges and replicates the arcs in the static BN into all the time-slices in the DBN |

motor | Multivariate time series dataset on the temperature of an electric motor |

mvn_inference | Performs inference over a multivariate normal distribution |

node_levels | Defines a level for every node in the net |

Particle | R6 class that defines a Particle in the PSO algorithm |

plot_dynamic_network | Plots a dynamic Bayesian network in a hierarchical way |

plot_network | Plots a Bayesian networks in a hierarchical way |

Position | R6 class that defines DBNs as causality lists |

pos_minus_pos_cpp | Substracts two Positions to obtain the Velocity that transforms one into the other |

pos_plus_vel_cpp | Add a velocity to a position |

predict_bn | Performs inference over a fitted GBN |

predict_dt | Performs inference over a test data set with a GBN |

PsoCtrl | R6 class that defines the PSO controller |

psoho | Learn a DBN structure with a PSO approach |

randomize_vl_cpp | Randomize a velocity with the given probabilities |

rename_nodes_cpp | Return a list of nodes with the time slice appended up to the desired size of the network |

time_rename | Renames the columns in a data.table so that they end in '_t_0' |

Velocity | R6 class that defines velocities affecting causality lists in the PSO |

vel_plus_vel_cpp | Add two Velocities |