Bayesian Networks & Path Analysis
Executes a bootstrap during the learning of a BN structure
Verifies the BN learning algorithms
Verify if one specific variable of a data set is dichotomic
Check the levels of a categorical variable
Verify variables with NA
Verify if one specific variable of a data set is an ordered factor
Verifies if there are ordered factor variables to be declared in the p...
Indentifies and gives an option to remove outliers
Verify the type of one variable
Verify types of variable
Check if the variables need to be ordered
Converts the position of any element of confusion matrix to VP, FP, FN...
Create a Parallel Socket Cluster
Creates dummy variables in the data set and remove master variables
Learn the Bayesian Network structure from data and build a PA model
Generates PA input model
Generates a PA model
Mounts a white or black list
Builds a black list of predictor and/or outcome variable
Extract information of outliers
Transform categorical variables into ordinal
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>.