Commit f992854e authored by Gilles Kratzer's avatar Gilles Kratzer
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......@@ -10,7 +10,7 @@ Authors@R: c(person("Gilles", "Kratzer", role = c("aut", "cre"),
Author: Gilles Kratzer [aut, cre] (<https://orcid.org/0000-0002-5929-8935>),
Reinhard Furrer [ctb] (<https://orcid.org/0000-0002-6319-2332>)
Maintainer: Gilles Kratzer <gilles.kratzer@math.uzh.ch>
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) <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 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.
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 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.
Depends: R (>= 3.5.0)
License: GPL-3
Encoding: UTF-8
......@@ -20,3 +20,4 @@ Suggests: bnlearn, knitr, rmarkdown, ggdag
RoxygenNote: 6.1.1
VignetteBuilder: knitr
URL: https://www.math.uzh.ch/pages/mcmcabn/
BugReports: https://git.math.uzh.ch/gkratz/mcmcabn/issues
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......@@ -12,6 +12,6 @@ install.packages("https://git.math.uzh.ch/gkratz/mcmcabn/raw/master/mcmcabn_0.1.
## 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) <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 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.
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.
## Future implementations (ordered by urgency)
......@@ -10,7 +10,7 @@
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<script src="pkgdown.js"></script><meta property="og:title" content="Flexible Implementation of a Structural MCMC Sampler for DAGs">
<meta property="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) &lt;https://doi.org/10.1007/s10994-008-5057-7&gt; and the Markov blanket resampling from Su and Borsuk (2016) &lt;http://jmlr.org/papers/v17/su16a.html&gt;. It supports three priors: a prior controlling for structure complexity from Koivisto and Sood (2004) &lt;http://dl.acm.org/citation.cfm?id=1005332.1005352&gt;, 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.">
<meta property="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) &lt;doi.10.1007/s10994-008-5057-7&gt; and the Markov blanket resampling from Su and Borsuk (2016) &lt;http://jmlr.org/papers/v17/su16a.html&gt;. It supports three priors: a prior controlling for structure complexity from Koivisto and Sood (2004) &lt;http://dl.acm.org/citation.cfm?id=1005332.1005352&gt;, 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.">
<meta name="twitter:card" content="summary">
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</div>
<div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
<div class="license">
<div class="links">
<h2>Links</h2>
<ul class="list-unstyled">
<li>Report a bug at <br><a href="https://git.math.uzh.ch/gkratz/mcmcabn/issues">https://​git.math.uzh.ch/​gkratz/​mcmcabn/​issues</a>
</li>
</ul>
</div>
<div class="license">
<h2>License</h2>
<ul class="list-unstyled">
<li><a href="https://www.r-project.org/Licenses/GPL-3">GPL-3</a></li>
......
......@@ -3,4 +3,7 @@ pkgdown: 1.3.0
pkgdown_sha: ~
articles:
mcmcabn: mcmcabn.html
urls:
reference: https://www.math.uzh.ch/pages/mcmcabn//reference
article: https://www.math.uzh.ch/pages/mcmcabn//articles
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