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Falsificationist Bayesianism?

The following are my thoughts on the paper “Beyond subjective and objective in statistics” by Gelman & Hennig (JRSS A, 2017), which was read at an ordinary meeting of the RSS on Wednesday. Overall, I really liked the paper. From the title and abstract, I was worried that it was either going to be a pointless philosophical argument of Bayes vs. frequentist, or else a statement of the obvious, but it was neither. In fact, the authors argue against tribalism in statistics and attempt to provide some universal guidelines for statistical practice.

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Fixing compile issues on SPARC Solaris

Time for a new version of my R package, to fix some compile errors on Solaris. It irks me that the R Core Team insists on supporting a crufty compiler on an obsolete OS, when there is no support for Intel Parallel Studio on Windows. Even Microsoft R Open only supports GCC. I’m hoping that the new Windows Subsystem for Linux (WSL) might finally provide an option for compiling R packages with nVidia CUDA on Windows, but I haven’t had a chance to investigate yet. In the meantime, this post describes how I fixed the compile errors as well as another NOTE that had appeared in CRAN. These changes are available in version 0.4-0 of bayesImageS.

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Warwick R User Group

I gave a talk last Thursday at the Warwick R Users’ Group (WRUG), a regular meeting that is held during term time in the stats department. Some of this was a rehash of a previous talk I gave at BRAG, updated for the 2011 edition of the National Land Cover Database (NLCD). But I also discussed how to download and import data from MODIS and Landsat 8. My slides are below and the R source code is available from the WRUG homepage.

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Fitting nonlinear functions in R

Following up on a post by Markus Gesmann, I wanted to look at logistic growth curves with a known inflection point. This is an example of functional data analysis with widespread applications, such as population dynamics and pharmacokinetics. Mages’ blog looked at the dugongs data from a textbook (Ratkowsky, 1983), which was subsequently analysed by Carlin & Gelfand (1991) and included in Vol. II of the BUGS manual as well as the Stan user guide. Markus compared point estimates from the R function nlm() with Bayesian inference using Stan. The methods were in close agreement with each other, as well as with the Gibbs sampler of Carlin & Gelfand. This made me curious to explore beyond this simple example, building towards the generalised logistic function that is a solution to the ordinary differential equation (ODE) of Richards (1959).

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Toolchain for macOS Sierra

Previously, I’ve described my setup on Windows 7 and macOS 10.9.x (Mavericks). Now that I’ve got a new MacBook Air, it’s time to update these instructions for macOS 10.12.x (Sierra). The setup described below is quite minimal, since I have limited disk space. See the article by Bhaskar Karambelkar‏ for an install based on homebrew that has all the bells & whistles.

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Reading DICOM-RT in R using RadOnc

Previously I’ve written my own R code to access DICOM-RT structure sets in group 3006 of the meta-data. Shortly after I wrote that original post, Reid F. Thompson made his R package RadOnc available on CRAN. Unfortunately, my old code no longer works with the current version of the oro.dicom R package, therefore I would recommend using RadOnc instead. The code below is focused on importing the 3D geometry, but the R package has a lot of other features that you might find useful: for example, calculation of Dice similarity coefficient and Hausdorff distance; as well as import of dose-volume histograms (DVH).

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This is a follow up to my previous post about the Swendsen-Wang (SW) algorithm, where I mentioned that SW has better convergence properties than Gibbs when the inverse temperature parameter β is large. This difference can be quantified by initialising the two algorithms at known starting points and measuring how many iterations it takes to converge. This is the second in a series of posts describing the functions and algorithms that I have implemented in the R package bayesImageS, which is now available on CRAN.

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