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 gmail.com>
License:	GPL-3
URL:	https://sites.google.com/site/bnparp/.
NeedsCompilation:	no
CRAN checks:	bnpa results

Documentation:

Reference manual:

bnpa.pdf

Downloads:

Package source:	bnpa_0.3.0.tar.gz
Windows binaries:	r-devel: bnpa_0.3.0.zip, r-release: bnpa_0.3.0.zip, r-oldrel: bnpa_0.3.0.zip
macOS binaries:	r-release (arm64): bnpa_0.3.0.tgz, r-oldrel (arm64): bnpa_0.3.0.tgz, r-release (x86_64): bnpa_0.3.0.tgz, r-oldrel (x86_64): bnpa_0.3.0.tgz
Old sources:	bnpa archive

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