My new book Computational Methods for Numerical Analysis with R came out today from Chapman & Hall/CRC Press. This book is a long-standing project of mine–originally started in 2001, and then using Octave as the base language. As the world didn’t need yet another book on doing numerical analysis in Octave, and therefore MATLAB, I eventually moved it to R. Computational Methods for Numerical Analysis with R is an overview of traditional numerical analysis topics presented using R. This guide shows how common functions from linear algebra, interpolation, numerical integration, optimization, and differential equations can be implemented in pure R
The Journal of Statistical Software has published another review I wrote, this time of Monogan’s Political Analysis Using R: No Title No Description The book is a solid choice for a primary or supplementary text in a political or policy methodology class, at the level of advanced undergraduate or first-year graduate student. You can get more information from Springer’s website: Political Analysis Using R | James E. Monogan III | Springer This book provides a narrative of how R can be useful in the analysis of public administration, public policy, and political science data specifically, in…
David Smith and I are now talking to each other in blog posts and it is only a little weird. Also, I’ve been traveling and am a bit behind. In a comment on this post, he notes this: I suspect the reason why R Core adopted the 0^0=1 definition is because of the binomial justification, R being a stats package after all. I can’t think of any defense for NaN^0=1 though… Well, it turns out there’s a good reason. If we go back C, and try an experiment, we can observe the following example produces these results: Compiling and executing
R has two different ways of representing missing data and understanding each is important for the user. NaN means “not a number” and it means there is a result, but it cannot be represented in the computer. The second, NA, explains that the data is just missing for unknown reasons. These appear at different times when working with R and each has different implications. NaN is distinct from NA. NaN implies a result that cannot be calculated for whatever reason, or is not a floating point number. Some calculations that lead to NaN, other than , are attempting to take
Version 0.7.3 of the Phonics software package posted to CRAN overnight. This new version includes support for the Cologne (Kölner) phonetic spelling algorithm. Cologne had been in for about a month, but this is the first release including it. As always, you can report bugs via GitHub. Image by Pixel-mixer / Pixabay. I will eventually run out of free yak photos online. But not yet!
Sometime, approximately forever ago, I put together a small package to produce waterfall charts in R. It provided two functions, one using base graphics and one using lattice graphics. I planned to document this and also create a version in ggplot. I never got around to either and through bit rot, and an ever evolving packaging system within R, the project fell out of compliance and was kicked off CRAN. About a year ago, I converted the Mercurial repository to git and posted it to GitHub. As a part of this, I also introduced Git Flow into the tree. But