Blog Archives

New features in imager 0.20

May 2, 2016
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New features in imager 0.20

imager, an R package for image processing, has been updated to v0.20 on CRAN. It’s a major upgrade with a lot of new features, better documentation and a more consistent API. imager now has 130 functions, and I myself keep forgetting all that’s in there. I’ve added a tutorial vignette that should help you get

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New R package for Eyelink eye-trackers

February 23, 2016
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New R package for Eyelink eye-trackers

Eyelink eye-trackers output an avalanche of disorganised crap. I’ve written an R package that will hopefully filter that crap for you. It’s called eyelinker and it’s on Github. It outputs a set of dataframes containing raw traces, saccades, fixations and blinks, meaning it’s easy to produce plots like this one: There’s a vignette explaining everything,

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imager now on CRAN, and a non-linear filtering example

September 17, 2015
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imager now on CRAN, and a non-linear filtering example

imager is an R package for image processing that’s fairly fast and now quite powerful (if I may say so myself). It wraps a neat C++ library called CImg, by David Tschumperlé (CNRS). It took quite a bit of work, but imager is now on CRAN, so that installing it is as easy as: Here’s

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New package for image processing in R

June 5, 2015
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New package for image processing in R

I’ve written a package for image processing in R, with the goal of providing a fast API in R that lets you do things in C++ if you need to. The package is called imager, and it’ on Github. The whole thing is based on CImg, a very nice C++ library for image processing by

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cMDS: visualising changing distances

November 11, 2013
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cMDS: visualising changing distances

Gina Gruenhage has just arxived a new paper describing an algorithm we call cMDS. Here’s what it’s for: if you do any kind of data analysis you often find yourself comparing datapoints using some kind of distance metric. All’s well if you have a unique reasonable distance metric you can use, but often what you

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ECVP tutorial on classification images

August 30, 2013
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ECVP tutorial on classification images

The slides for my ECVP tutorial on classification images are available here. Try this alternative version if the equations look funny. (image from Mineault et al. 2009) The slides are in HTML and contain some interactive elements. They’re the result of experimenting with R Markdown, D3 and pandoc. You write the slides in R Markdown,

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Fitting psychometric functions using STAN

August 19, 2013
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Fitting psychometric functions using STAN

STAN is a new system for Bayesian inference, similar to BUGS and JAGS. I’ve played with it a bit and it’s quite promising, it really has the potential to make MCMC less of a pain (on simple models). I’ve written a short introduction to fitting psychometric functions using STAN and R, in case that’s useful

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Predicting spatial locations using point processes

June 25, 2013
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Predicting spatial locations using point processes

I’ve uploaded a draft tutorial on some aspects of prediction using point processes. I wrote it using R-Markdown, so there’s bits of R code for readers to play with. It’s hosted on Rpubs, which turns out to be a great deal more convenient than WordPress for that sort of thing.

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Finding patterns in time series using regular expressions

May 17, 2013
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Finding patterns in time series using regular expressions

Regular expressions are a fantastic tool when you’re looking for patterns in time series. I wish I’d realised that sooner. Here’s a timely example: traditionally, when you have two successive quarters of negative GDP growth, you’re in recession. We have a quarterly GDP time series for Australia, and we want to know how many recessions

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Barycentric interpolation: fast interpolation on arbitrary grids

March 6, 2013
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Barycentric interpolation: fast interpolation on arbitrary grids

Barycentric interpolation generalises linear interpolation to arbitrary dimensions. It is very fast although suboptimal if the function is smooth. You might now it as algorithm 21.7.1 in Numerical Recipes (Two-dimensional Interpolation on an Irregular Grid). Using package geometry it can be implemented in a few lines of code in R. Here’s a quick explanation of what

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