Venue: Sion em Keldenich, Weyertal 47, 50937 Cologne, Germany, 6 p.m., 30 March 2012, View Larger MapFor more details and registration see the Kölner R User Group page.

David Varadi have recently wrote two posts about Gini Coefficient: I Dream of Gini, and Mean-Gini Optimization. I want to show how to use Gini risk measure to construct efficient frontier and compare it with alternative risk measures I discussed previously. I will use Gini mean difference risk measure – the mean of the difference

Ed Chen is a data scientist at Twitter, so he's accustomed to working with big data and complex models. In an interview with MIT Technology Review, he describes his data science toolbox: A common pattern for me is that I'll code a MapReduce job in Scala, do some simple command-line munging on the results, pass the data into Python...

I’m in the process of remaking all the metadata from scratch and looking once again at the question of UHI. There are not any global conclusions we can draw from the data yet; I’m just in the process of checking out everything that is available that could be used to illuminate the problem. The problem,

In the first part of this article we built a function (rocdata) to calculate the co-ordinates for the ROC plot and its summary statistics. Now we need to actually produce the plot. I make most of my plots in ggplot2 because of it’s versatility. However there’s no reason why these plots couldn’t be produced

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