Package: bnpa
Type: Package
Title: Bayesian Networks & Path Analysis
Version: 0.3.0
Imports: bnlearn, fastDummies, lavaan, Rgraphviz, semPlot, xlsx
Author: Elias Carvalho, Joao R N Vissoci, Luciano Andrade, Wagner Machado, Emerson P Cabrera, Julio C Nievola
Maintainer: Elias Carvalho <ecacarva@gmail.com>
Description: 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>.
URL: https://sites.google.com/site/bnparp/.
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-08-01 01:57:45 UTC; elias
Repository: CRAN
Date/Publication: 2019-08-01 23:20:02 UTC
Built: R 4.6.0; ; 2026-04-30 08:13:23 UTC; unix
