By Mark Sellors – Senior IT Consultant, UK It’s been a few months now since I presented on the ways in which Mango are using Docker at EARL 2014 and a lot’s happened since then, so I thought I take … Continue reading →

By Mark Sellors – Senior IT Consultant, UK It’s been a few months now since I presented on the ways in which Mango are using Docker at EARL 2014 and a lot’s happened since then, so I thought I take … Continue reading →

Jay & I cover dashboards in Chapter 10 of Data-Driven Security (the book) but have barely mentioned them on the blog. That’s about to change with a new series on building dashboards using the all-new shinydashboard framework developed by RStudio. While we won’t duplicate the full content from the book, we will show different types of...

You’ve heard that graphics processing units — GPUs — can bring big increases in computational speed. While GPUs cannot speed up work in every application, the fact is that in many cases it can indeed provide very rapid computation. In this tutorial, we’ll see how this is done, both in passive ways (you write only … Continue reading...

In this post I will run SAS example Logistic Regression Random-Effects Model in four R based solutions; Jags, STAN, MCMCpack and LaplacesDemon. To quote the SAS manual: 'The data are taken from Crowder (1978). The Seeds data set is a 2 x 2 fa...

Welcome to the third part of series posts. In previous post, I discussed about the data points which we require to perform predictive analysis. In this post I will discuss about the solution approach along with required methodology and its implementation in R. Before we move ahead in this part, let us recall the prediction The post Predictive...

Last week's post about the Kalman filter focused on the derivation of the algorithm. Today I will continue with the extended Kalman filter (EKF) that can deal also with nonlinearities. According to Wikipedia the EKF has been considered the de facto standard in the theory of nonlinear state estimation, navigation systems and GPS.Kalman filterI had the following...

Interactive visualization allows deeper exploration of data than static plots. Javascript libraries such as d3 have made possible wonderful new ways to show data. Luckily the R community has been active in developing R interfaces to some popular javascript libraries to enable R users to create interactive visualizations without knowing any javascript. In this post I have reviewed...

Mozilla released the MetricsGraphics.js library back in November of 2014 (gh repo) and was greeted with great fanfare. It’s primary focus is on crisp, clean layouts for interactive time-series data, but they have support for other chart types as well (though said support is far from comprehensive). I had been pondering building an R package

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