bnpa (0.3.0)

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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., & Lbre, 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. .

Maintainer: Elias Carvalho
Author(s): Elias Carvalho, Joao R N Vissoci, Luciano Andrade, Wagner Machado, Emerson P Cabrera, Julio C Nievola

License: GPL-3

Uses: bnlearn, fastDummies, lavaan, semPlot, xlsx

Released 6 months ago.

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