Dynamic Bayesian Network Learning and Inference

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Documentation for package ‘dbnR’ version 0.7.5

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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
as_named_vector Converts a single row data.table into a named vector
bn_translate_exp Experimental function that translates a natPosition vector into a DBN network.
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.
create_natcauslist_cpp Create a natural causal list from a DBN. This is the C++ backend of the function.
crop_names_cpp If the names of the nodes have "_t_0" appended at the end, remove it
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_inference_backwards Performs exact inference smoothing 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)
filtered_fold_dt Fold a dataset to a certain size and avoid overlapping of different time-series
filter_same_cycle Filter the instances in a data.table that have values of different ids in each row
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
generate_random_network_exp Experimental function that generates a random DBN and samples a dataset that defines it
initialize_cl_cpp Create a causality list and initialize it
init_cl_cpp Initialize the nodes vector
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
natCauslist This file contains all the classes needed for the natPSOHO structure learning algorithm. It was implemented as an independent package in https://github.com/dkesada/natPSOHO and then merged into dbnR. All the original source files are merged into one to avoid bloating the R/ folder of the package.
natParticle R6 class that defines a Particle in the PSO algorithm
natPosition R6 class that defines DBNs as vectors of natural numbers
natPsoCtrl R6 class that defines the PSO controller
natPsoho Learn a DBN structure with a PSO approach
natVelocity R6 class that defines velocities in the PSO
nat_cl_to_arc_matrix_cpp Create a matrix with the arcs defined in a causlist object
nat_cte_times_vel_cpp Multiply a Velocity by a constant real number
nat_pos_minus_pos_cpp Subtracts two natPositions to obtain the natVelocity that transforms ps1 into ps2
nat_pos_plus_vel_cpp Add a velocity to a position
nat_vel_plus_vel_cpp Adds two natVelocities
nodes_gen_exp Generates the names of the nodes in t_0 and in all the network
node_levels Defines a level for every node in the net
one_hot One hot encoder for natural numbers without the 0.
one_hot_cpp One-hot encoder for natural numbers without the 0
ordering_gen_exp Generates the names of n variables.
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 or a dynamic Bayesian network
plot_static_network Plots a Bayesian networks in a hierarchical way
Position R6 class that defines DBNs as causality lists
pos_minus_pos_cpp Subtracts 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
recount_arcs_exp Experimental function that recounts the number of arcs in the position
reduce_freq Reduce the frequency of the time series data in a data.table
rename_nodes_cpp Return a list of nodes with the time slice appended up to the desired size of the network
smooth_ts Performs smoothing with the GDBN over a data set
time_rename Renames the columns in a data.table so that they end in '_t_0'
trunc_geom Geometric distribution sampler truncated to a maximum
Velocity R6 class that defines velocities affecting causality lists in the PSO
vel_plus_vel_cpp Add two Velocities