I honestly have no book on R programming. In fact I have not a single book on programming at all (my coding proves that ;x). I am pretty sure that I am gonna order (just did!) that book. You can get a look of Matloff’s text here (= pdf for ya)

The R programming language has become one of the standard tools for statistical data analysis and visualization, and is widely used by Google and many others. The language includes extensive support for working with vectors of integers, numerics (doubles), and many other types, but has lacked support for 64-bit integers. ...

The following function, color.palette(), is a wrapper for colorRampPalette() and allows some increased flexibility in defining the spacing between main color levels. One defines both the main color levels (as with colorRampPalette) and an optional vector containing the number of color levels that should be put in between at equal distances. The above...

The following is a function for the calculation of Empirical Orthogonal Functions (EOF). For those coming from a more biologically-oriented background and are familiar with Principal Component Analysis (PCA), the methods are similar. In the climate sciences the method is usually used for the decomposition of a data field into dominant spatial-temporal modes. Read...

The Objective I wanted to source R scripts hosted on my github repository for use in my blog (i.e. a github version of ?source). This would make it easier for anyone wishing to test out my code snippets on their own computers without having to manually go to my github repo and retrieve a series of R

This post is somewhat marginal to R in that there are several statistical systems that could be used to tackle the problem. Bayesian statistics is one of those topics that I would like to understand better, much better, in fact. … Continue reading →

Matthew Hoffman and Andrew Gelman have posted a paper on arXiv entitled “The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo” and developing an improvement on the Hamiltonian Monte Carlo algorithm called NUTS (!). Here is the abstract: Hamiltonian Monte Carlo (HMC) is a Markov chain Monte Carlo (MCMC) algorithm that avoids the

At the DecisionStats blog, Ajay Ohri has published his review of Revolution R Enterprise 5.0. The review includes a slideshow highlighting some of the features of the new release, including the expanded code snippets manager and the new cluster job manager. It's well worth checking out if you'd like a quick overview of what's new in the latest release....

When I was making the slides for a lecture on using Sweave to incorporate R and LaTeX I was unpleasantly surprised at how tedious it can be to extract statistical values and print them in proper LaTeX code. For example, consider a … Continue reading →

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