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 |
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