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>.

Version: 0.3.0
Imports: bnlearn, fastDummies, lavaan, Rgraphviz, semPlot, xlsx
Published: 2019-08-01
Author: Elias Carvalho, Joao R N Vissoci, Luciano Andrade, Wagner Machado, Emerson P Cabrera, Julio C Nievola
Maintainer: Elias Carvalho <ecacarva at>
License: GPL-3
NeedsCompilation: no
CRAN checks: bnpa results


Reference manual: bnpa.pdf
Package source: bnpa_0.3.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release: bnpa_0.3.0.tgz, r-oldrel: bnpa_0.3.0.tgz
Old sources: bnpa archive


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