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...

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

“Behind every great point estimate stands a minimized loss function.” – Me, just now This is a continuation of Probable Points and Credible Intervals, a series of posts on Bayesian point and interval estimates. In Part 1 we looked at these estimates as graphical summaries, useful when it’s difficult to plot the whole posterior in good way....

R has featured packages to support GPU programming for over five years. Beginning with the Gputools package, developers continue to introduce new and more sophisticated tools to take advantage of these powerful coprocessors. The gmatrix package is a recent continuation of this trend which offers some significant new features. Background GPUs, or graphical processing units, are the numerical engines powering the visual display of modern computers....

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