Package: dbnR 0.7.9

dbnR: Dynamic Bayesian Network Learning and Inference

Learning and inference over dynamic Bayesian networks of arbitrary Markovian order. Extends some of the functionality offered by the 'bnlearn' package to learn the networks from data and perform exact inference. It offers three structure learning algorithms for dynamic Bayesian networks: Trabelsi G. (2013) <doi:10.1007/978-3-642-41398-8_34>, Santos F.P. and Maciel C.D. (2014) <doi:10.1109/BRC.2014.6880957>, Quesada D., Bielza C. and Larrañaga P. (2021) <doi:10.1007/978-3-030-86271-8_14>. It also offers the possibility to perform forecasts of arbitrary length. A tool for visualizing the structure of the net is also provided via the 'visNetwork' package.

Authors:David Quesada [aut, cre], Gabriel Valverde [ctb]

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dbnR.pdf |dbnR.html✨
dbnR/json (API)
NEWS

# Install 'dbnR' in R:
install.packages('dbnR', repos = c('https://dkesada.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/dkesada/dbnr/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • motor - Multivariate time series dataset on the temperature of an electric motor

On CRAN:

bayesian-networksdynamic-bayesian-networksforecastinginferencetime-series

4.95 score 48 stars 37 scripts 720 downloads 23 exports 6 dependencies

Last updated 5 months agofrom:f8745ceac1. Checks:OK: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 16 2024
R-4.5-win-x86_64OKNov 16 2024
R-4.5-linux-x86_64OKNov 16 2024
R-4.4-win-x86_64OKNov 16 2024
R-4.4-mac-x86_64OKNov 16 2024
R-4.4-mac-aarch64OKNov 16 2024

Exports:calc_mucalc_sigmadegreefilter_same_cyclefiltered_fold_dtfit_dbn_paramsfold_dtforecast_tsgenerate_random_network_explearn_dbn_strucmvn_inferencenodesnodes<-plot_dynamic_networkplot_static_networkpredict_bnpredict_dtrbn.dbn.fitreduce_freqscoreshift_valuessmooth_tstime_rename

Dependencies:bnlearndata.tablemagrittrMASSR6Rcpp

Readme and manuals

Help Manual

Help pageTopics
Replacement function for parameters inside DBNs[[<-.dbn.fit
Replacement function for parameters inside DBNs$<-.dbn.fit
Calculate the AIC of a dynamic Bayesian networkAIC.dbn
Calculate the AIC of a dynamic Bayesian networkAIC.dbn.fit
Check if two network structures are equal to each otherall.equal.dbn
Check if two fitted networks are equal to each otherall.equal.dbn.fit
Convert a network structure into a model stringas.character.dbn
Calculate the BIC of a dynamic Bayesian networkBIC.dbn
Calculate the BIC of a dynamic Bayesian networkBIC.dbn.fit
Calculate the mu vector from a fitted BN or DBNcalc_mu
Calculate the sigma covariance matrix from a fitted BN or DBNcalc_sigma
Extracts the coefficients of a DBNcoef.dbn.fit
Calculates the degree of a list of nodesdegree
Filter the instances in a data.table with different ids in each rowfilter_same_cycle
Fold a dataset avoiding overlapping of different time seriesfiltered_fold_dt
Fits a markovian n DBN modelfit_dbn_params
Extracts the fitted values of a DBNfitted.dbn.fit
Widens the dataset to take into account the t previous time slicesfold_dt
Performs forecasting with the GDBN over a datasetforecast_ts
Generate a random DBN and a sampled datasetgenerate_random_network_exp
Learns the structure of a markovian n DBN model from datalearn_dbn_struc
Calculate the log-likelihood of a dynamic Bayesian networklogLik.dbn
Calculate the log-likelihood of a dynamic Bayesian networklogLik.dbn.fit
Average the parameters of multiple dbn.fit objects with identical structuresmean.dbn.fit
Multivariate time series dataset on the temperature of an electric motormotor
Performs inference over a multivariate normal distributionmvn_inference
Returns a list with the names of the nodes of a BN or a DBNnodes
Relabel the names of the nodes of a BN or a DBNnodes<-
Plots a dynamic Bayesian network in a hierarchical wayplot_dynamic_network
Plots a Bayesian network in a hierarchical wayplot_static_network
Plots a dynamic Bayesian networkplot.dbn
Plots a fitted dynamic Bayesian networkplot.dbn.fit
Performs inference over a fitted GBNpredict_bn
Performs inference over a test dataset with a GBNpredict_dt
Performs inference in every row of a dataset with a DBNpredict.dbn.fit
Print method for "dbn" objectsprint.dbn
Print method for "dbn.fit" objectsprint.dbn.fit
Simulates random samples from a fitted DBNrbn.dbn.fit
Reduce the frequency of the time series data in a data.tablereduce_freq
Returns the residuals from fitting a DBNresiduals.dbn.fit
Computes the score of a BN or a DBNscore
Move the window of values backwards in a folded dataset rowshift_values
Returns the standard deviation of the residuals from fitting a DBNsigma.dbn.fit
Performs smoothing with the GDBN over a datasetsmooth_ts
Renames the columns in a data.table so that they end in '_t_0'time_rename