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. . Nagarajan, R., Scutari, M., & L├Ębre, S. (2013). Bayesian networks in r. Springer, 122, 125-127. Scutari, M., & Denis, J. B. . Scutari M (2010). Bayesian networks: with examples in R. Chapman and Hall/CRC. . Rosseel, Y. (2012). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software, 48(2), 1 - 36. .


Reference manual

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0.3.0 by Elias Carvalho, 2 years ago

Browse source code at

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

Documentation:   PDF Manual  

GPL-3 license

Imports bnlearn, fastDummies, lavaan, Rgraphviz, semPlot, xlsx

See at CRAN