R package mcmcabn: Flexible Implementation of a Structural MCMC Sampler for DAGs. Flexible implementation of a structural MCMC sampler for Directed Acyclic Graphs (DAGs). It supports the new edge reversal move from Grzegorczyk and Husmeier (2008) <doi:10.1007/s10994-008-5057-7> and the Markov blanket resampling from Su and Borsuk (2016) <http://jmlr.org/papers/v17/su16a.html>. It supports three priors: a prior controlling for structure complexity from Koivisto and Sood (2004) <http://dl.acm.org/citation.cfm?id=1005332.1005352>, an uninformative prior and a user defined prior. The three main problems that can be addressed by this R package are selecting the most probable structure based on a cache of pre-computed scores, controlling for overfitting and sampling the landscape of high scoring structures. It allows to quantify the marginal impact of relationships of interest by marginalising out over structures or nuisance dependencies. Structural MCMC seems a very elegant and natural way to estimate the true marginal impact, so one can determine if it’s magnitude is big enough to consider as a worthwhile intervention.
Keywords for this software
References in zbMATH (referenced in 2 articles )
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- Polina Suter, Jack Kuipers, Giusi Moffa, Niko Beerenwinkel: Bayesian structure learning and sampling of Bayesian networks with the R package BiDAG (2021) arXiv
- Gilles Kratzer, Fraser Iain Lewis, Arianna Comin, Marta Pittavino, Reinhard Furrer: Additive Bayesian Network Modelling with the R Package abn (2019) arXiv