In case you missed it: October 2014 Roundup
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In case you missed them, here are some articles from October of particular interest to R users.
R hits a new milestone with 6,000 CRAN packages, and R 3.1.2 released.
Revolution Analytics announces Revolution R Open, a supported and enhanced downstream distribution of R. (Learn more at the webinar on Wednesday November 12.)
Some benchmarks on the performance improvements that come from linking Revolution R Open with the Intel Math Kernel Libraries.
Now available: the Reproducible R Toolkit: a package (“checkpoint”) and a server containing archived CRAN packages to make it easy to reproduce the results of R code that uses packages.
Revolution Analytics has released DeployR Open, a new open-source framework for integrating R into other applications.
The new MRAN website provides a dependency graph for every R package on CRAN.
A new package miniCRAN makes it easy to create a local package repository with a subset of CRAN packages.
The ACM held an unconference near San Francisco, and featured a comparison of principal components analysis in R and Python.
The author of the survival package, Dr Terry Therneau, on the state of Type III tests in R.
Using R to create a “fashion fingerprint” to visualize colors in a fashion collection.
The new “Rocker” project provides easy-to-use Docker containers (similar to virtual machines) including R.
HP releases “Distributed R”, an R package to integrate with the HP Vertica database.
Some tips on controlling R resource usage when deployed in a production environment.
An exhortation to explore complex (and even not-so-complex) statistical problems with simulation.
Presentations from R user groups on image analysis, data mapping and data journalism.
The Fantasy Football Analytics blog suggests 14 reasons why R is better than Excel for data analysis.
An overview of the Tweedie distribution, which judging from citations is becoming more widely used.
A look at the GLDEX package and the Generalized Lambda Distribution to model financial returns.
In a video interview, RStudio's Joe Cheng discusses how the design of the R language supports the implementation of domain-specific languages.
Slides from the webinar “R and Data Science“, presented by Joseph Rickert.
An article in the New York Times contrasts Bayesian and Frequentist Statistics.
General interest stories (not related to R) in the past month included: a Halloween prank, teaching robots to walk with a genetic algorithm, if dogs and cats kept diaries, Ikea's “bookbook” and the direction techniques of David Fincher.
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