Package: dbnlearn 0.1.0

dbnlearn: Dynamic Bayesian Network Structure Learning, Parameter Learning and Forecasting

It allows to learn the structure of univariate time series, learning parameters and forecasting. Implements a model of Dynamic Bayesian Networks with temporal windows, with collections of linear regressors for Gaussian nodes, based on the introductory texts of Korb and Nicholson (2010) <doi:10.1201/b10391> and Nagarajan, Scutari and Lèbre (2013) <doi:10.1007/978-1-4614-6446-4>.

Authors:Robson Fernandes [aut, cre, cph]

dbnlearn_0.1.0.tar.gz
dbnlearn_0.1.0.zip(r-4.7-any)dbnlearn_0.1.0.zip(r-4.6-any)dbnlearn_0.1.0.zip(r-4.5-any)
dbnlearn_0.1.0.tgz(r-4.6-any)dbnlearn_0.1.0.tgz(r-4.5-any)
dbnlearn_0.1.0.tar.gz(r-4.7-any)dbnlearn_0.1.0.tar.gz(r-4.6-any)
dbnlearn_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
dbnlearn/json (API)

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

Bug tracker:https://github.com/robson-fernandes/dbnlearn/issues

On CRAN:

Conda:

bayesian-inferencebayesian-networksdynamic-bayesian-networksprobabilistic-graphical-modelstime-series

4.46 score 22 stars 26 scripts 360 downloads 4 exports 57 dependencies

Last updated from:85b4c06549. Checks:7 ERROR, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develERROR125
source / vignettesOK169
linux-releaseERROR119
macos-releaseERROR105
macos-oldrelERROR127
windows-develERROR67
windows-releaseERROR75
windows-oldrelERROR85
wasm-releaseOK120

Exports:dbn.fitdbn.learndbn.predictdbn.preprocessing

Dependencies:assertthatbase64encbnlearnbnviewerbslibcachemclicommonmarkcpp11digestevaluatefarverfastmapfontawesomefsggplot2gluegtablehighrhtmltoolshtmlwidgetshttpuvigraphisobandjquerylibjsonliteknitrlabelinglaterlatticelifecyclemagrittrMatrixmemoisemimeotelpkgconfigpromisesR6rappdirsRColorBrewerRcpprlangrmarkdownS7sassscalesshinysourcetoolstinytexvctrsviridisLitevisNetworkwithrxfunxtableyaml