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<h1 class="title toc-ignore">Software</h1>
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<p><link rel="stylesheet" href="academicons.css"/></p>
<p><br></p>
<div id="software" class="section level3">
<h3>SOFTWARE</h3>
<p>A selection of R packages I have developed. For other code and
analysis scripts, see my <a href="http://github.com/angelgar/">GitHub
profile</a>.</p>
<hr />
<div id="afpca-adaptive-functional-principal-component-analysis"
class="section level4">
<h4>afpca — Adaptive Functional Principal Component Analysis</h4>
<p>An R package for estimating directions of variation in functional
data that exhibit sharp changes in smoothness. <code>afpca</code>
combines a fast, scalable adaptive scatterplot smoother with a
probabilistic FPCA framework, allowing each functional principal
component to be smoothed adaptively. This is particularly useful in
settings such as neural recordings, where sharp post-stimulus
transitions must be distinguished from smooth baseline behavior —
settings where standard global-smoothness assumptions fail.</p>
<ul>
<li>Source: <a
href="https://github.com/angelgar/afpca">github.com/angelgar/afpca</a></li>
<li>Documentation & vignettes: <a
href="http://angelgarciadelagarza.com/afpca/">angelgarciadelagarza.com/afpca</a></li>
<li>Co-developed with <a href="https://sauerbreilab.org/">Britton
Sauerbrei</a> and <a href="https://jeffgoldsmith.com/">Jeff
Goldsmith</a>.</li>
<li>Companion manuscript: <a
href="https://arxiv.org/abs/2310.01760">Adaptive Smoothing Functional
Principal Component Analysis</a> (under review at
<em>Biometrics</em>).</li>
</ul>
<p>Install the development version from GitHub:</p>
<pre class="r"><code># install.packages("devtools")
devtools::install_github("angelgar/afpca")</code></pre>
<p><strong>A quick visual tour.</strong> Below are example outputs from
the package vignette. First, 20 simulated curves drawn from a mean
function and two functional principal components with locally varying
smoothness:</p>
<p style="text-align:center;">
<img src="https://github.com/angelgar/afpca/raw/master/man/figures/README-simulated_data_plot-1.png" alt="Simulated functional data with locally varying smoothness" style="width:100%; max-width:700px;"/>
</p>
<p>The estimated mean function and the first two adaptively-smoothed
functional principal components recovered by <code>fpca.adapt()</code> —
note how the estimator preserves sharp post-onset transitions rather
than over-smoothing them:</p>
<p style="text-align:center;">
<img src="https://github.com/angelgar/afpca/raw/master/man/figures/README-estimated_fpc_plot-1.png" alt="Estimated mean function and adaptively-smoothed functional principal components" style="width:100%; max-width:700px;"/>
</p>
<p>And two examples of observed curves alongside their reconstructions
from the estimated components:</p>
<p style="text-align:center;">
<img src="https://github.com/angelgar/afpca/raw/master/man/figures/README-reconstructed_plots-1.png" alt="Observed curves with adaptive FPCA reconstructions" style="width:100%; max-width:700px;"/>
</p>
<hr />
</div>
<div id="voxel-mass-univariate-voxelwise-analysis-of-imaging-data"
class="section level4">
<h4>voxel — Mass-Univariate Voxelwise Analysis of Imaging Data</h4>
<p>An R package for mass-univariate voxelwise analysis of NIfTI
medical-imaging data, supporting general linear and generalized additive
model workflows at the voxel level.</p>
<ul>
<li>CRAN: <a
href="https://cran.r-project.org/web/packages/voxel/">voxel</a> —
35,000+ downloads</li>
<li>Co-developed with <a
href="https://www.vumc.org/biostatistics/person/simon-vandekar-phd">Simon
Vandekar</a>, <a href="https://www.satterthwaitelab.com/">Ted
Satterthwaite</a>, and <a
href="https://www.pennmedicine.org/cancer/about/clinical-trials/cancer-clinical-research-team/biostatistics/taki-shinohara">Taki
Shinohara</a>.</li>
</ul>
<p>Install from CRAN:</p>
<pre class="r"><code>install.packages("voxel")</code></pre>
<p style="text-align:center;">
<img src="images/figure-voxel.pdf" alt="voxel package example output" style="width:100%; max-width:700px;"/>
</p>
<p><br></p>
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