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<h2 id="setup-clugenr-development">Setup <code>clugenr</code> development</h2>
<p>Clone the package, <code>cd</code> into to the package’s folder and start R:</p>
<pre><code class="language-plaintext">$ git clone https://github.com/clugen/clugenr.git
$ cd clugenr
</code></pre>
<p>Developing <code>clugenr</code> requires <a href="https://devtools.r-lib.org/">devtools</a>, thus make sure it’s installed before
continuing. Open an R prompt at the <code>clugenr</code> folder and install <code>clugenr</code>’s
development dependencies:</p>
<pre><code class="language-R">devtools::install_dev_deps()
</code></pre>
<p>The package can be tested as follows:</p>
<pre><code class="language-R">devtools::test()
</code></pre>
<p>There are four levels of testing, by increasing thoroughness (and slowness):
<code>cran</code>, <code>ci</code>, <code>normal</code> and <code>full</code>. The first two run by default on CRAN and CI
environments, respective [... truncated]
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<p>This vignette contains a number of examples on how to use <code>clugenr</code> in 2D.
Examples require the following setup code:</p>
<pre><code class="language-r"># Load the clugenr library
library(clugenr)

# Load functions for plotting examples
source(&quot;plot_examples_2d.R&quot;, local = knitr::knit_global())

# Keep examples reproducible in newer R versions
RNGversion(&quot;3.6.0&quot;)
</code></pre>
<p>The 2D examples were plotted with the <code>plot_examples_2d()</code> function available
<a href="https://github.com/clugen/clugenr/blob/main/vignettes/plot_examples_2d.R">here</a>.</p>
<h2 id="manipulating-the-direction-of-cluster-supporting-lines">Manipulating the direction of cluster-supporting lines</h2>
<h3 id="using-the-direction-parameter">Using the <code>direction</code> parameter</h3>
<pre><code class="language-r">seed &lt;- 123
</code></pre>
<pre><code class="language-r">e001 &lt;- clugen(2, 4, 200, c(1, 0), 0, c(10, 10 [... truncated]
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<p>This section contains several examples on how to merge cluster data, either
generated with <strong>clugenr</strong> or from other sources. Although it is possible to
merge data in any dimension, these examples will focus on merging 2D data.
Therefore, we’ll use the same <code>plot_examples_2d()</code> function used for the
<code>vignette(&quot;examples2d&quot;)</code>, requiring the following setup code:</p>
<pre><code class="language-r"># Load the clugenr library
library(clugenr)

# Load functions for plotting examples
source(&quot;plot_examples_2d.R&quot;, local = knitr::knit_global())

# Keep examples reproducible in newer R versions
RNGversion(&quot;3.6.0&quot;)
</code></pre>
<h2 id="merging-two-data-sets-generated-with-clugen">Merging two data sets generated with <code>clugen()</code></h2>
<pre><code class="language-r">seed1 &lt;- 4444
seed2 &lt;- 5555
</code></pre>
<pre><code class="language-r">e090 &lt;- clugen(2, 5, 1000, c(1 [... truncated]
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<p>This vignette contains a number of examples on how to use <code>clugenr</code> in 1D and
5D. Examples require the following setup code:</p>
<pre><code class="language-r"># Load the clugenr library
library(clugenr)

# Load functions for plotting examples
source(&quot;plot_examples_1d.R&quot;, local = knitr::knit_global())
source(&quot;plot_examples_nd.R&quot;, local = knitr::knit_global())

# Keep examples reproducible in newer R versions
RNGversion(&quot;3.6.0&quot;)
</code></pre>
<h2 id="basic-1d-example-with-density-plot">Basic 1D example with density plot</h2>
<p>The 1D examples were plotted with the <code>plot_examples_1d()</code> function available
<a href="https://github.com/clugen/clugenr/blob/main/vignettes/plot_examples_1d.R">here</a>.</p>
<pre><code class="language-r">seed &lt;- 222
</code></pre>
<pre><code class="language-r"># Custom proj_dist_fn: point projections placed using the Weibull distribution
proj_wbull &lt;- funct [... truncated]
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<p>This vignette describes the theoretical framework of the <em>clugen</em> algorithm,
starting with a general <a href="#overview">Overview</a>, then moving on to a
<a href="#detailed-description">Detailed description</a>.</p>
<h2 id="overview">Overview</h2>
<p><em>Clugen</em> is an algorithm for generating multidimensional clusters. Each cluster
is supported by a line segment, the position, orientation and length of which
guide where the respective points are placed. For brevity, <em>line segments</em> will
be referred to as <em>lines</em>.</p>
<p>Given an \(n\)-dimensional direction vector \(\mathbf{d}\) (and a number of
additional parameters, which will be discussed shortly), the <em>clugen</em> algorithm
works as follows ($^*$ means the algorithm step is stochastic):</p>
<ol>
<li>Normalize \(\mathbf{d}\).</li>
<li>\(^*\)Determine cluster sizes.</li>
<li>\(^*\)Determine cluster centers.</li>
<li>\(^*\)Determine lengths of cluster-supp [... truncated]
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