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) <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 userdefined 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.

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) <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 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.

The package provides a flexible implementation of a structural MCMC sampler for Directed Acyclic Graphs (DAGs). It supports the new edge reversal move from Grzegorczyk and Husmeier (2008) <https://doi.org/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 userdefined 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.

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 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.

<script src="../pkgdown.js"></script><metaproperty="og:title"content="mcmcabn: a structural MCMC sampler for DAGs learned from observed systemic datasets">

<script src="../pkgdown.js"></script><metaproperty="og:title"content="mcmcabn: A Structural Mcmc Sampler for Dags Learned from Observed Systemic Datasets">

<script src="pkgdown.js"></script><metaproperty="og:title"content="Flexible Implementation of a Structural MCMC Sampler for DAGs">

<metaproperty="og:description"content="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 userdefined 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.">

<metaproperty="og:description"content="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 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.">

<ahref="#mcmcabn-a-structural-mcmc-sampler-for-dags-learned-from-observed-systemic-datasets"class="anchor"></a>mcmcabn: a structural MCMC sampler for DAGs learned from observed systemic datasets</h1></div>

<divid="quick-start"class="section level2">

<ahref="#mcmcabn-a-structural-mcmc-sampler-for-dags-learned-from-observed-systemic-datasets"class="anchor"></a>mcmcabn: A Structural Mcmc Sampler for Dags Learned from Observed Systemic Datasets</h1></div>

<p>To install <code>mcmabn</code> you need two R packages: <ahref="https://CRAN.R-project.org/package=abn">abn</a> and <ahref="https://CRAN.R-project.org/package=gRbase">gRbase</a> which requires libraries not stored on <ahref="https://cran.r-project.org/">CRAN</a> but on <ahref="http://www.bioconductor.org/">bioconductor</a>. Hence you <strong>must</strong> install these packages <strong>before</strong> installing <code>mcmcabn</code>:</p>

<li>sampling the landscape of high scoring structures.</li>

</ul>

<p>The latter could be very useful in an applied perspective to avoid reducing the richeness of Bayesian network modelling to report only <strong>one</strong> structure. Indeed, it allows user 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.</p>

<p>The latter could be beneficial in an applied perspective to avoid reducing the richness of Bayesian network modeling to report only <strong>one</strong> structure. Indeed, it allows the user to quantify the marginal impact of relationships of interest by marginalizing 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.</p>

<p>mcmcabn is a flexible implementation of a structural MCMC sampler for Directed Acyclic Graphs (DAGs). It supports the new edge reversal move from Grzegorczyk and Husmeier (2008) <ahref="https://doi.org/10.1007/s10994-008-5057-7"class="uri">https://doi.org/10.1007/s10994-008-5057-7</a> and the Markov blanket resampling from Su and Borsuk (2016) <ahref="http://jmlr.org/papers/v17/su16a.html"class="uri">http://jmlr.org/papers/v17/su16a.html</a>. It supports three priors: a prior controlling for structure complexity from Koivisto and Sood (2004) <ahref="http://dl.acm.org/citation.cfm?id=1005332.1005352"class="uri">http://dl.acm.org/citation.cfm?id=1005332.1005352</a>, an uninformative prior and a userdefined 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.</p>

<p>Flexible implementation of a structural MCMC sampler for Directed Acyclic Graphs (DAGs). It supports the new edge reversal move from Grzegorczyk and Husmeier (2008) <ahref="doi:10.1007/s10994-008-5057-7"class="uri">doi:10.1007/s10994-008-5057-7</a> and the Markov blanket resampling from Su and Borsuk (2016) <ahref="http://jmlr.org/papers/v17/su16a.html"class="uri">http://jmlr.org/papers/v17/su16a.html</a>. It supports three priors: a prior controlling for structure complexity from Koivisto and Sood (2004) <ahref="http://dl.acm.org/citation.cfm?id=1005332.1005352"class="uri">http://dl.acm.org/citation.cfm?id=1005332.1005352</a>, 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.</p>

<spanclass='co'>## This data set was generated using the following code:</span>

<spanclass='fu'><ahref='https://rdrr.io/r/base/library.html'>library</a></span>(<spanclass='no'>bnlearn</span>) <spanclass='co'>#for the dataset</span>

<spanclass='fu'><ahref='https://rdrr.io/r/base/library.html'>library</a></span>(<spanclass='no'>abn</span>) <spanclass='co'>#for the cache of scores computing function</span>

<spanclass='co'>## This data set was generated using the following code:</span>

<spanclass='fu'><ahref='https://rdrr.io/r/base/library.html'>library</a></span>(<spanclass='no'>bnlearn</span>) <spanclass='co'>#for the dataset</span>

<spanclass='fu'><ahref='https://rdrr.io/r/base/library.html'>library</a></span>(<spanclass='no'>abn</span>) <spanclass='co'>#for the cache of score function</span>

# mcmcabn: a structural MCMC sampler for DAGs learned from observed systemic datasets

# mcmcabn: A Structural Mcmc Sampler for Dags Learned from Observed Systemic Datasets

## Quickstart

## Quickstart

To install `mcmabn` you need two R packages: [abn](https://CRAN.R-project.org/package=abn) and [gRbase](https://CRAN.R-project.org/package=gRbase) which requires libraries not stored on [CRAN](https://cran.r-project.org/) but on [bioconductor](http://www.bioconductor.org/). Hence you **must** install these packages **before** installing `mcmcabn`:

...

...

@@ -26,11 +26,11 @@ The three main problems addressed by this R package are:

- controlling for overfitting.

- sampling the landscape of high scoring structures.

The latter could be very useful in an applied perspective to avoid reducing the richeness of Bayesian network modelling to report only **one** structure. Indeed, it allows user 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.

The latter could be beneficial in an applied perspective to avoid reducing the richness of Bayesian network modeling to report only **one** structure. Indeed, it allows the user to quantify the marginal impact of relationships of interest by marginalizing 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.

## Description

mcmcabn is a flexible implementation of a structural MCMC sampler for Directed Acyclic Graphs (DAGs). It supports the new edge reversal move from Grzegorczyk and Husmeier (2008) <https://doi.org/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 userdefined 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.

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 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.