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mcmcabn
View on CRAN: Click
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Download and install mcmcabn package within the R console
Install from CRAN:
install.packages("mcmcabn")
Install from Github:
library("remotes")
install_github("cran/mcmcabn")
Install by package version:
library("remotes")
install_version("mcmcabn", "0.6")
Attach the package and use:
library("mcmcabn")
Maintained by
Annina Cincera
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[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-03-08
Latest Update: 2023-12-22
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) and the Markov blanket resampling from Su and Borsuk (2016) . It supports three priors: a prior controlling for structure complexity from Koivisto and Sood (2004) , 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.
How to cite:
Annina Cincera (2019). mcmcabn: Flexible Implementation of a Structural MCMC Sampler for DAGs. R package version 0.6, https://cran.r-project.org/web/packages/mcmcabn. Accessed 28 Jan. 2025.
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Functions, R codes and Examples using
the mcmcabn R package
Some associated functions: CoupledHeatedmcmcabn . abnCache_asia . dist-asia . mcmc . mcmc_asia . mcmcabn-package . plot . print-summary . print . query . summary .
Some associated R codes: MBR.R . REV.R . mc3.R . mcmc-heated.R . mcmc.R . mcmcabn-utiles.R . plot-mcmcabn.R . print-mcmcabn.R . print-summary-mcmcabn.R . query.R . summary-mcmcabn.R . Full mcmcabn package functions and examples
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