Additive Bayesian Networks Modeling

Additive Bayesian Networks (ABN) has been developed to disentangle complex relationships of highly correlated datasets as frequently encountered in risk factor analysis studies. ABN is an efficient approach to sort out direct and indirect relationships among variables which is surprisingly common in systemic epidemiology. After the tutorial, you will run the particular steps within an ABN analysis with real-world data. You will be able to contrast this approach with standard regression (linear, logistic, Poisson regression, and multinomial models) used for classical risk factor analysis.
Towards the end, we also cover Bayesian Model Averaging in the context of an ABN, which is useful to assess the validity of the learned model and more advanced inference on the network.

[WilliamNkegbe]

Please how do I contact you for personal support in the use of the ABN?
Emails to Gilles Kratzer and team receive blocked responses.

Also want to know how I can write by r codes for fitabn to "return a list of score for each node, the parameter estimates,
the standard deviation and the p-values" as contained in Kratzer et al., 2023.
Thank you.

[mikiallen]

But why not do quantile regression which would provide a more detailed sub-level/ sub-component analysis in to the most important risk factors related to each subsample ranked in quantile terms !