<rss version="2.0" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Hacker News: marcandre259</title><link>https://news.ycombinator.com/user?id=marcandre259</link><description>Hacker News RSS</description><docs>https://hnrss.org/</docs><generator>hnrss v2.1.1</generator><lastBuildDate>Mon, 08 Jun 2026 16:05:34 +0000</lastBuildDate><atom:link href="https://hnrss.org/user?id=marcandre259" rel="self" type="application/rss+xml"></atom:link><item><title><![CDATA[New comment by marcandre259 in "Show HN: PypoLCA – Latent Class Analysis and Regression in Python"]]></title><description><![CDATA[
<p>The project started as I wanted to learn more about the Expectation Maximisation (EM) algorithms. LCA's estimation algorithm is very similar to Gaussian Mixture Modeling, so it's a quite easy jump from popular online EM content.<p>The project translates the functionalities of venerable polca R package. It's also nice to have polca as a reference package to validate output.<p>I use Python at work, so the algorithm themselves are written in C++...</p>
]]></description><pubDate>Sun, 07 Jun 2026 12:07:13 +0000</pubDate><link>https://news.ycombinator.com/item?id=48434011</link><dc:creator>marcandre259</dc:creator><comments>https://news.ycombinator.com/item?id=48434011</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=48434011</guid></item><item><title><![CDATA[Show HN: pypoLCA – Latent Class Analysis and Regression in Python]]></title><description><![CDATA[
<p>Article URL: <a href="https://github.com/marcandre259/pypolca">https://github.com/marcandre259/pypolca</a></p>
<p>Comments URL: <a href="https://news.ycombinator.com/item?id=48434010">https://news.ycombinator.com/item?id=48434010</a></p>
<p>Points: 6</p>
<p># Comments: 1</p>
]]></description><pubDate>Sun, 07 Jun 2026 12:07:13 +0000</pubDate><link>https://github.com/marcandre259/pypolca</link><dc:creator>marcandre259</dc:creator><comments>https://news.ycombinator.com/item?id=48434010</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=48434010</guid></item></channel></rss>