Some days ago H. Wickham (Chief Scientist of the RStudio company) posted an article about the RStudio CRAN mirror with …
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General Regression Neural Network with R
Similar to the back propagation neural network, the general regression neural network (GRNN) is also a good tool for the function approximation in the modeling toolbox. Proposed by Specht in 1991, GRNN has advantages of instant training and easy tuning. A GRNN would be formed instantly with just a 1-pass training with the development data. 
Introducing GTrendsR
In a paper, to be soon published in Conservation Biology and entitle Googling trends in conservation biology, we developed a package named GTrendsR that provides an interface for retrieving and displaying the information returned online...
Thursday: Webinar on video game analytics
Video games are big business today: Electronic Arts (EA) generated more than 4 billion dollars in revenue last year, and they're not even the biggest player on the block. In addition to big bucks, video games also generate Big Data: 50 terabytes per day at EA alone. So there's an obvious need to apply predictive analytics to these massive...
Annotating select points on an X-Y plot using ggplot2
or, Is the Seattle Mariners outfield a disaster?
The Backstory
Earlier this week (2013-06-10), a blog post by Dave Cameron appeared at USS Mariner under the title “Maybe It's Time For Dustin Ackley To Play Some Outfield”. In the first paragraph, Cameron describes to the Seattle Mariners outfield this season as “a complete disaster” and Raul Ibanez as...Intro to Parallel Random Number Generation with RevoScaleR
by Joseph Rickert Random number generation is fundamental to doing computational statistics. As you might expect, R is very rich in random number resources. The R base code provides several high quality random number generators including: Wichmann-Hill, Marsaglia-Multicarry, Super-Duper, Mersenne-Twister, Knuth-TAOCP-2002 and L’Ecuyer-CMRG. (See Random for details.) And, there are at least three packages, rspring, rlecuyer, and rstream for...
Fixing My Internet With R and Python
Last summer, I had some internet connectivity problems. Specifically, I would have massive latency issues that affected my conversations on Skype and my relatively pathetic under the best of circumstances efforts at online gaming. It was driving me up a wall and I couldn't figure it out. It hadn't...
Major League Baseball run scoring trends with R’s Lahman package
(This article was first published on Bayes Ball, and kindly contributed to R-bloggers) The statistical software R has an ever-expanding array of packages that provide pre-programmed functions and datasets. One such package is named Lahman, bundling the contents of the Lahman database into a quick-and-easy resource for R users. In addition to the data tables, the package resources also...
Grid Search for Free Parameters with Parallel Computing
In my previous post (http://statcompute.wordpress.com/2013/05/25/test-drive-of-parallel-computing-with-r) on 05/25/2013, I’ve demonstrated the power of parallel computing with various R packages. However, in the real world, it is not straight-forward to utilize these powerful tools in our day-by-day computing tasks without carefully formulate the problem. In the example below, I am going to show how to use the 
Import All Text Files in A Folder with Parallel Execution
Sometimes, we might need to import all files, e.g. *.txt, with the same data layout in a folder without knowing each file name and then combine all pieces together. With the old method, we can use lapply() and do.call() functions to accomplish the task. However, when there are a large number of such files and 
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
