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    <title>Luke Hannan</title>
    <subtitle>Biostatistics notes on clinical trials, microbiome analysis, and reproducible research</subtitle>
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    <updated>2026-05-21T00:00:00+00:00</updated>
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    <entry xml:lang="en">
        <title>Hello World</title>
        <published>2026-05-21T00:00:00+00:00</published>
        <updated>2026-05-21T00:00:00+00:00</updated>
        
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        <content type="html" xml:base="/posts/hello-world/">&lt;p&gt;&lt;aside class=&quot;marginnote&quot;&gt;
  &lt;p&gt;Some recent examples:&lt;&#x2F;p&gt;
&lt;ul&gt;
&lt;li&gt;&lt;a rel=&quot;external&quot; href=&quot;https:&#x2F;&#x2F;opensource.posit.co&#x2F;blog&#x2F;2026-05-21_in-defense-of-yaml&#x2F;&quot;&gt;In Defense of YAML&lt;&#x2F;a&gt;&lt;&#x2F;li&gt;
&lt;li&gt;&lt;a rel=&quot;external&quot; href=&quot;https:&#x2F;&#x2F;kobzol.github.io&#x2F;rust&#x2F;python&#x2F;2023&#x2F;05&#x2F;20&#x2F;writing-python-like-its-rust.html&quot;&gt;Writing Python like it&#x27;s Rust&lt;&#x2F;a&gt;&lt;&#x2F;li&gt;
&lt;li&gt;&lt;a rel=&quot;external&quot; href=&quot;https:&#x2F;&#x2F;josiah.rs&#x2F;posts&#x2F;type-safety&#x2F;&quot;&gt;Type safe(r) R code&lt;&#x2F;a&gt;&lt;&#x2F;li&gt;
&lt;li&gt;&lt;a rel=&quot;external&quot; href=&quot;https:&#x2F;&#x2F;josiah.rs&#x2F;posts&#x2F;cross-language-ds&#x2F;&quot;&gt;Apache Arrow, Rust, and cross-langauge data science&lt;&#x2F;a&gt;&lt;&#x2F;li&gt;
&lt;&#x2F;ul&gt;
&lt;p&gt;and perhaps more controversially:&lt;&#x2F;p&gt;
&lt;ul&gt;
&lt;li&gt;&lt;a rel=&quot;external&quot; href=&quot;https:&#x2F;&#x2F;josiah.rs&#x2F;posts&#x2F;gpl-cost&#x2F;&quot;&gt;GPL is holding R back&lt;&#x2F;a&gt;&lt;&#x2F;li&gt;
&lt;&#x2F;ul&gt;

&lt;&#x2F;aside&gt;

These days I spend much of my time reading excellent blog posts about making data science and R faster (typically using Rust).
I have learnt a great deal from those posts, not just about computer science, but also about the value of writing. Both
for your readers and for yourself as well. Writing forces can sometimes be laborious, but it forces you to convert
your understanding of a thing into something that should (hopefully) be intelligible to your target audience.&lt;&#x2F;p&gt;
&lt;p&gt;Given the above, I&#x27;ve decided to attempt to get more practice at both understanding and explaining the tools and methods
I use regularly. I’ll mainly write for applied statisticians and data scientists working in R-heavy workflows.
Expect notes on biostatistics, clinical-trial thinking, and computational methods for microbiome and related omics data.&lt;&#x2F;p&gt;
&lt;p&gt;If any of the above sounds interesting to you, you&#x27;re welcome to follow along or to reach out to me:&lt;&#x2F;p&gt;
&lt;h2 id=&quot;elsewhere&quot;&gt;Elsewhere&lt;&#x2F;h2&gt;
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