R: Dynamic Bayesian Network Learning and Inference

# Dynamic Bayesian Network Learning and Inference

## Documentation for package ‘dbnR’ version 0.8.0

- DESCRIPTION file
- Package NEWS
- Package README

## Help Pages

$<-.dbn.fit | Replacement function for parameters inside DBNs
AIC.dbn | Calculate the AIC of a dynamic Bayesian network
AIC.dbn.fit | Calculate the AIC of a dynamic Bayesian network
all.equal.dbn | Check if two network structures are equal to each other
all.equal.dbn.fit | Check if two fitted networks are equal to each other
as.character.dbn | Convert a network structure into a model string
BIC.dbn | Calculate the BIC of a dynamic Bayesian network
BIC.dbn.fit | Calculate the BIC of a dynamic Bayesian network
calc_mu | Calculate the mu vector from a fitted BN or DBN
calc_sigma | Calculate the sigma covariance matrix from a fitted BN or DBN
coef.dbn.fit | Extracts the coefficients of a DBN
degree | Calculates the degree of a list of nodes
filtered_fold_dt | Fold a dataset avoiding overlapping of different time series
filter_same_cycle | Filter the instances in a data.table with different ids in each row
fitted.dbn.fit | Extracts the fitted values of a DBN
fit_dbn_params | Fits a markovian n DBN model
fold_dt | Widens the dataset to take into account the t previous time slices
forecast_ts | Performs forecasting with the GDBN over a dataset
generate_random_network_exp | Generate a random DBN and a sampled dataset
learn_dbn_struc | Learns the structure of a markovian n DBN model from data
logLik.dbn | Calculate the log-likelihood of a dynamic Bayesian network
logLik.dbn.fit | Calculate the log-likelihood of a dynamic Bayesian network
mean.dbn.fit | Average the parameters of multiple dbn.fit objects with identical structures
motor | Multivariate time series dataset on the temperature of an electric motor
mvn_inference | Performs inference over a multivariate normal distribution
nodes | Returns a list with the names of the nodes of a BN or a DBN
nodes<- | Relabel the names of the nodes of a BN or a DBN
plot.dbn | Plots a dynamic Bayesian network
plot.dbn.fit | Plots a fitted dynamic Bayesian network
plot_dynamic_network | Plots a dynamic Bayesian network in a hierarchical way
plot_static_network | Plots a Bayesian network in a hierarchical way
predict.dbn.fit | Performs inference in every row of a dataset with a DBN
predict_bn | Performs inference over a fitted GBN
predict_dt | Performs inference over a test dataset with a GBN
print.dbn | Print method for "dbn" objects
print.dbn.fit | Print method for "dbn.fit" objects
rbn.dbn.fit | Simulates random samples from a fitted DBN
reduce_freq | Reduce the frequency of the time series data in a data.table
residuals.dbn.fit | Returns the residuals from fitting a DBN
score | Computes the score of a BN or a DBN
shift_values | Move the window of values backwards in a folded dataset row
sigma.dbn.fit | Returns the standard deviation of the residuals from fitting a DBN
smooth_ts | Performs smoothing with the GDBN over a dataset
time_rename | Renames the columns in a data.table so that they end in '_t_0'
[[<-.dbn.fit | Replacement function for parameters inside DBNs