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.
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).
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.
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).
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.
My R package was rejected the first time, due to an old bug in RcppArmadillo (details below). I also forgot to add ‘cran-comments.html’ to my .Rbuildignore after following Hadley Wickham’s otherwise excellent advice on how to develop a package for CRAN. The source package and Windows binaries are now available, with OS X soon to follow. Using R-hub was definitely helpful, since it allowed me to test my package on various flavours of Linux and versions of R before submitting it. The NOTEs didn’t come as a surprise, since running rhub::check_for_cran had already made me aware of them. Hopefully R-hub will add support for Mac OS and SPARC Solaris soon. My code has multiple compile errors in Solaris Studio 12.3, which would be painful to fix without access to a virtual machine. Continuous integration with Travis might also have been useful, but my code is hosted on Bitbucket not GitHub.