- Do you know anything about the Hilbert Matrix (other than it is probably named after David Hilbert)? In his post this week, Nicholas Horton, Professor of Statistics at Amherst College, explains what it is, and how to create these matrices using both SAS and R.
- Xi’an discusses a new paper by Randal Douc, Florian Maire, and Jimmy Olsson called MCMC for sampling from mixture models.
- Some popular statistical articles this week are: Modeling Data With Functional Programming In R by Cartesian Faith, Make your ggplots shareable, collaborative, and with D3 by Matt Sundquist, Implementing a Principal Component Analysis (PCA) by Sebastian Raschka (for Python), and Ordering Datasets Alphabetically by geomorph.
- And finally, have you ever tried the popular mobile game 2048? If not, here are some code that you can run on your machine and start playing the game with R.
- R 3.1.0 (codename “Spring Dance“) is released this week!
- Do you invest in the stock market? If so, you may know the so-called 60/40 rule (invest 40% in bonds and 60% in stocks). But do you really believe this strategy? Eran Raviv performs some simulation studies and tries to verify whether the 60/40 rule is a wise choice or simply a myth.
- Popular R articles of the week: “Pretty” table columns and Calculating confidence intervals for proportions by Alan Haynes (Insights of a PhD student), Interpreting interaction coefficient in R by Lionel H. (biologyforfun) and Extract CSV data from PDF files with Tabula by Nathan Yau (Flowingdata).
- And finally, the most loyal fans in the NBA are…
- To give this year’s April Fools’ day a more analytical touch, here are The 7 Funniest Data Cartoons.
- Xi’an discusses a new paper by Scott Schmidler and his Ph.D. student Douglas VanDerwerken called Parallel MCMC.
- Tim Harford of Financial Times shares his thoughts on Big Data in an article called Big data: are we making a big mistake?
- David Springate publishes three very useful R articles this week: Develop in RStudio, run in RScript, Functional programming in R, and Two R tutorials for beginners.
- And finally, Revolution Analytics summarizes some recent news and reports on how the rise of the “R” computer language brings open source to science.
- Are you a fan of Wes Anderson? Revoluntion Analytics shares some ideas on how you can bring his style to your own R charts, by making use of these Wes Anderson inspired palettes.
- Given 3 random variables X, Y and Z with known distributions, can you calculate cov(X, Y) from cov(X, Z) and cov(Y, Z)?
- Some useful R tips this week are: Filtering Data with L1 Regularisation, quickly calculating summary statistics from a data frame, A Simple Introduction to the Graphing Philosophy of ggplot2, and Visualizing principal components with R and Sochi Olympic Athletes.
- Xi’an reviews Bayesian Data Analysis by Andrew Gelman, John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Don Rubin.
- And finally, Nathan Yau of FlowingData presents some visuals from a study on smoking prevalence from 1996 to 2012, and concludes that smoking rate is inversely proportional to income level.
- James Paul Peruvankal of Revoluntion Analytics shares the secrets of teaching R. Joseph Rickert of the same organization publishes some online sources to download data sets in his article called Data Sets for Data Science.
- Some interesting R related articles this week are: Species occurrence data by Karthik Ram of rOpenSci, barplot with ggplot2 by Martin Johnsson (PhD student at Linköping University), Stop using bivariate correlations for variable selection and The German Tank Problem: The Frequentist Way by Jacob Simmering (PhD student at University of Iowa), MCMC for Econometrics Students by Professor David Giles of University of Victoria (part I, part II and part III), Normality and Testing for Normality by Thomas Hopper (aka Learning as You Go), and It is time for RData files to become the standard for Data Transfer by Francis Smart (PhD student at Michigan State University).
- Xi’an discusses his new paper (with Matthew Moores and Kerrie Mengersen) called Pre-processing for approximate Bayesian computation in image analysis.
- And finally, the Royal Statistical Society publishes the Timeline of Statistics – a timeline with illustrations and texts that covers major events in the world of statistics starting from 450 BC.