Dynamic Bayesian Network Learning and Inference


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Documentation for package ‘dbnR’ version 0.5.3

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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