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: 2026-07-03 19:02:54 UTC; root
Config/pak/sysreqs: cmake libglpk-dev make default-jdk libicu-dev
        libjpeg-dev libpng-dev libuv1-dev libxml2-dev zlib1g-dev
Repository: https://eliascarvalho.r-universe.dev
Date/Publication: 2019-08-01 22:20:02 UTC
RemoteUrl: https://github.com/cran/bnpa
RemoteRef: HEAD
RemoteSha: 3f27ca031b7f6fbf30264d8582970f505f8b76ea