Commit b46bc1e2 authored by Gilles Kratzer's avatar Gilles Kratzer
Browse files

website

parent a14aed36
Pipeline #1656 passed with stage
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......@@ -60,6 +60,24 @@ mcmcabn <- function(score.cache = NULL, score = "mlik", data.dists = NULL, max.p
dag.tmp <- dag.tmp[order(name.dag), order(name.dag)]
}
if (is.character(start.dag) && start.dag == "hc"){
start.dag <- search.heuristic(score.cache,
score,
data.dists,
max.parents,
num.searches = 100,
max.steps = 500,
seed,
verbose,
start.dag = NULL,
dag.retained = NULL,
dag.banned = NULL,
algo = "hc",
tabu.memory = 10,
temperature = 0.9)
}
if (is.matrix(start.dag)) {
start.dag[start.dag != 0] <- 1
diag(start.dag) <- 0
......
......@@ -8,7 +8,7 @@
prob.rev, prob.mbr, prior.choice) {
# start tests
if (is.null(score.cache))
stop("A cache of score should be provided. YOu can produce it using the R package abn.")
stop("A cache of score should be provided. You can produce it using the R package abn.")
if (max(rowSums(score.cache$node.defn)) < max.parents)
stop("Check max.parents. It should be the same as the one used in abn::buildscorecache() R function")
......
......@@ -49,7 +49,7 @@ ___
## What's New
- 23/04/2019 - mcmcabn is available on CRAN (v 0.1)
- ../../2019 - mcmcabn is available on CRAN (v 0.1)
- 18/02/2019 - new pre-print [Is a single unique Bayesian network enough to accurately represent your data?](https://arxiv.org/pdf/1902.06641.pdf) on arXiv
......@@ -57,4 +57,3 @@ ____
**`mcmcabn` is developed and maintained by [Gilles Kratzer](https://gilleskratzer.netlify.com/) and [Prof. Dr. Reinhard Furrer](https://user.math.uzh.ch/furrer/) from
[Applied Statistics Group](https://www.math.uzh.ch/as/index.php?id=as) from the University of Zurich.**
___
......@@ -4,6 +4,8 @@ url: https://www.math.uzh.ch/pages/mcmcabn/
authors:
Gilles Kratzer:
href: "https://gilleskratzer.netlify.com/"
Reinhard Furrer:
href: "https://user.math.uzh.ch/furrer/"
navbar:
type: inverse
......
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......@@ -153,7 +153,7 @@ R package version 0.1, <a href="https://CRAN.R-project.org/package=mcmcabn">http
</p>
</li>
<li>
<p><strong>Reinhard Furrer</strong>. Contributor. <a href='https://orcid.org/0000-0002-6319-2332' target='orcid.widget'><img src='https://members.orcid.org/sites/default/files/vector_iD_icon.svg' class='orcid' alt='ORCID' height='16'></a>
<p><strong><a href='https://user.math.uzh.ch/furrer/'>Reinhard Furrer</a></strong>. Contributor. <a href='https://orcid.org/0000-0002-6319-2332' target='orcid.widget'><img src='https://members.orcid.org/sites/default/files/vector_iD_icon.svg' class='orcid' alt='ORCID' height='16'></a>
</p>
</li>
</ul>
......
......@@ -123,11 +123,11 @@ BiocManager<span class="op">::</span><span class="kw"><a href="https://www.rdocu
<h2 class="hasAnchor">
<a href="#whats-new" class="anchor"></a>What’s New</h2>
<ul>
<li><p>23/04/2019 - mcmcabn is available on CRAN (v 0.1)</p></li>
<li><p>../../2019 - mcmcabn is available on CRAN (v 0.1)</p></li>
<li><p>18/02/2019 - new pre-print <a href="https://arxiv.org/pdf/1902.06641.pdf">Is a single unique Bayesian network enough to accurately represent your data?</a> on arXiv</p></li>
</ul>
<hr>
<p><strong><code>mcmcabn</code> is developed and maintained by <a href="https://gilleskratzer.netlify.com/">Gilles Kratzer</a> and <a href="https://user.math.uzh.ch/furrer/">Prof. Dr. Reinhard Furrer</a> from <a href="https://www.math.uzh.ch/as/index.php?id=as">Applied Statistics Group</a> from the University of Zurich.</strong> ___</p>
<p><strong><code>mcmcabn</code> is developed and maintained by <a href="https://gilleskratzer.netlify.com/">Gilles Kratzer</a> and <a href="https://user.math.uzh.ch/furrer/">Prof. Dr. Reinhard Furrer</a> from <a href="https://www.math.uzh.ch/as/index.php?id=as">Applied Statistics Group</a> from the University of Zurich.</strong></p>
</div>
</div>
</div>
......
......@@ -179,7 +179,7 @@
</tr>
<tr>
<th>start.dag</th>
<td><p>a DAG given as a matrix, see details for format, which can be used to explicitly provide a starting point for the structural search. Alternatively character "random" will select a random DAG as starting point.</p></td>
<td><p>a DAG given as a matrix, see details for format, which can be used to explicitly provide a starting point for the structural search. Alternatively character "random" will select a random DAG as starting point. Character "hc" will call a hill-climber to select a DAG as starting point.</p></td>
</tr>
<tr>
<th>prior.dag</th>
......
......@@ -32,7 +32,7 @@ mcmcabn(score.cache = NULL,
\item{mcmc.scheme}{a sampling scheme. It is vector giving in that order: the number of returned DAGS, the number of thinned steps and length of the burn in phase.}
\item{seed}{a non-negative integer which sets the seed.}
\item{verbose}{extra output, see output for details.}
\item{start.dag}{a DAG given as a matrix, see details for format, which can be used to explicitly provide a starting point for the structural search. Alternatively character "random" will select a random DAG as starting point.}
\item{start.dag}{a DAG given as a matrix, see details for format, which can be used to explicitly provide a starting point for the structural search. Alternatively character "random" will select a random DAG as starting point. Character "hc" will call a hill-climber to select a DAG as starting point.}
\item{prior.dag}{user defined prior. It should be given as a matrix where entries range from zero to one. 0.5 is non-informative for the given arc.}
\item{prior.lambda}{hyper parameter representing the strength of belief in the user defined prior.}
\item{prob.rev}{probability of selecting a new edge reversal.}
......
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