Package: bnpa 0.3.0

bnpa: Bayesian Networks & Path Analysis

This project aims to enable the method of Path Analysis to infer causalities from data. For this we propose a hybrid approach, which uses Bayesian network structure learning algorithms from data to create the input file for creation of a PA model. The process is performed in a semi-automatic way by our intermediate algorithm, allowing novice researchers to create and evaluate their own PA models from a data set. The references used for this project are: Koller, D., & Friedman, N. (2009). Probabilistic graphical models: principles and techniques. MIT press. <doi:10.1017/S0269888910000275>. Nagarajan, R., Scutari, M., & Lèbre, S. (2013). Bayesian networks in r. Springer, 122, 125-127. Scutari, M., & Denis, J. B. <doi:10.1007/978-1-4614-6446-4>. Scutari M (2010). Bayesian networks: with examples in R. Chapman and Hall/CRC. <doi:10.1201/b17065>. Rosseel, Y. (2012). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software, 48(2), 1 - 36. <doi:10.18637/jss.v048.i02>.

Authors:Elias Carvalho, Joao R N Vissoci, Luciano Andrade, Wagner Machado, Emerson P Cabrera, Julio C Nievola

bnpa_0.3.0.tar.gz
bnpa_0.3.0.zip(r-4.5)bnpa_0.3.0.zip(r-4.4)
bnpa_0.3.0.tgz(r-4.4-any)
bnpa_0.3.0.tar.gz(r-4.5-noble)bnpa_0.3.0.tar.gz(r-4.4-noble)
bnpa_0.3.0.tgz(r-4.4-emscripten)
bnpa.pdf |bnpa.html
bnpa/json (API)

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

Peer review:

Uses libs:
  • openjdk– OpenJDK Java runtime, using Hotspot JIT
Datasets:
  • dataQualiN - A qualitative data set to test functions
  • dataQuantC - A quantiative data set to test functions

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.66 score 46 scripts 187 downloads 21 exports 120 dependencies

Last updated 5 years agofrom:3f27ca031b. Checks:OK: 1 NOTE: 4. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 15 2024
R-4.5-winNOTENov 15 2024
R-4.5-linuxNOTENov 15 2024
R-4.4-winNOTENov 15 2024
R-4.4-macNOTENov 15 2024

Exports:boot.strap.bncheck.algorithmscheck.dichotomic.one.varcheck.levels.one.variablecheck.nacheck.ordered.one.varcheck.ordered.to.pacheck.outlierscheck.type.one.varcheck.typescheck.variables.to.be.orderedconvert.confusion.matrixcreate.clustercreate.dummiesgera.bn.structuregera.pagera.pa.modelmount.wl.bl.listoutcome.predictor.varpreprocess.outlierstransf.into.ordinal

Dependencies:abindarmbackportsbase64encBHBiocGenericsbnlearnbootbslibcachemcarDatacheckmatecliclustercodacolorspacecorpcorcpp11data.tabledigestevaluatefansifarverfastDummiesfastmapfdrtoolfontawesomeforeignFormulafsgenericsggplot2glassoglueGPArotationgraphgridExtragtablegtoolshighrHmischtmlTablehtmltoolshtmlwidgetsigraphisobandjpegjquerylibjsonliteknitrkutilslabelinglatticelavaanlifecyclelisrelToRlme4magrittrMASSMatrixmemoisemgcvmimimeminqamnormtmunsellmvtnormnlmenloptrnnetnumDerivOpenMxopenxlsxpbapplypbivnormpillarpkgconfigplyrpngpsychqgraphquadprogR6rappdirsRColorBrewerRcppRcppEigenRcppParallelreshape2RgraphvizrJavarlangrmarkdownrockchalkrpartrpfrstudioapiRUnitsassscalessemsemPlotStanHeadersstringistringrtibbletinytexutf8vctrsviridisviridisLitewithrxfunxlsxxlsxjarsXMLxtableyamlzip