<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Revolution-Analytics on Yoke Keong</title><link>https://yokekeong.com/blog/revolution-analytics/</link><description>Recent content in Revolution-Analytics on Yoke Keong</description><generator>Hugo</generator><language>en</language><lastBuildDate>Tue, 16 Dec 2014 11:00:38 +0000</lastBuildDate><atom:link href="https://yokekeong.com/blog/revolution-analytics/index.xml" rel="self" type="application/rss+xml"/><item><title>Speeding up R</title><link>https://yokekeong.com/speeding-up-r/</link><pubDate>Tue, 16 Dec 2014 11:00:38 +0000</pubDate><guid>https://yokekeong.com/speeding-up-r/</guid><description>&lt;p&gt;When processing large datasets, some R commands operations requiring linear algebra libraries may run very slowly. As such you might want to try &lt;a href="http://mran.revolutionanalytics.com/open/"&gt;Revolution R Open&lt;/a&gt;, an enhanced R distribution using multi-threaded libraries. You can see some benchmarks &lt;a href="http://www.r-bloggers.com/r-r-with-atlas-r-with-openblas-and-revolution-r-open-which-is-fastest/"&gt;here&lt;/a&gt; and &lt;a href="http://blog.revolutionanalytics.com/2014/10/revolution-r-open-mkl.html"&gt;here&lt;/a&gt;.&lt;/p&gt;</description></item></channel></rss>