Description: 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) <https://jmlr.org/papers/v17/su16a.html>. It supports three priors: a prior controlling for structure complexity from Koivisto and Sood (2004) <https://dl.acm.org/doi/10.5555/1005332.1005352>, 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 us to quantify the marginal impact of relationships of interest by marginalizing out over structures or nuisance dependencies. Structural MCMC seems an 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.

Authors@R: c(person("Gilles", "Kratzer", role = c("aut"),
Imports: abn, coda, gRbase, ggplot2, cowplot, ggpubr
Author: Gilles Kratzer [aut] (<https://orcid.org/0000-0002-5929-8935>),

person("Joe", "Thorley", role = c("aut", "cre"), email = "joe@poissonconsulting.ca", comment = c(ORCID = "0000-0002-7683-4592"))
Description: Generates derived parameter(s) from Monte Carlo Markov Chain (MCMC)
samples using R code. This allows Bayesian models to be fitted without the
inclusion of derived parameters which add unnecessary clutter and
slow model fitting. For more information on MCMC samples see
Imports: abind, err, checkr, mcmcr, purrr
Suggests: covr, coda, plyr, doParallel, testthat

Imports: chk, abind, universals, extras, term, nlist, mcmcr (>= 0.3.0),

Description: Generates derived parameter(s) from Monte Carlo
Markov Chain (MCMC) samples using R code. This allows Bayesian models
to be fitted without the inclusion of derived parameters which add
unnecessary clutter and slow model fitting. For more information on
MCMC samples see Brooks et al. (2011) <isbn:978-1-4200-7941-8>.
Imports: abind, chk, extras, mcmcr, nlist, purrr, universals
Suggests: coda, covr, doParallel, plyr, testthat