I’ve made an R package, and I’m happy to report that it is likely to arrive on CRAN before Christmas! The last CRAN review asked me to change a comma, so the outlook is good.
mcp has grown quite ambitious and I think it now qualifies as a general-purpose package to analyze change points, superseding the other change point packages in most aspects. You can read a lot more about mcp in the extensive documentation at the mcp website.
This Twitter thread is a more bite-sized introduction:
Followed by this update:
mcp is the consequence of a long sequence of events:
- Looking at Complex Span data from a large project I did with researchers from Aarhus University, I saw strong performance discontinuities at a single-person level.
- I then learned that performance discontinuities play an important role in the estimation of (human) working memory capacity.
- I then learned that people mostly use ad-hoc methods to extract such points, including eye-balling graphs. That made me nervous.
- I then remembered a very small example from the book Bayesian Cognitive Modeling on inferring change points. I remember it because I was puzzled that Gibbs samplers could sample such points effectively.
- I then applied this to a Poisson model of our data and it worked beautifully. It outperformed all other models of this data.
- Around here was the first time I looked for other R packages to identify change points. There are a lot of them, but none could handle the hierarchical model I needed and most of them yielded point estimates rather than posteriors.
- As I began writing a paper, decided to apply the model to subitizing as well. However, while subitizing accuracy has a single change point, subitizing reaction times seem to have two or three.
- I implemented a three-change-points model, and immediately saw how easy it would be to extend it to N change points. That took around a day. That was the time of the first tweet above.
- Without much forethought, I took a brief look into R packages, pkgdown, etc., and before I knew it, it became a package.
- I began to see a roadmap for the package around the fall holiday, and spent most evenings there re-factoring so that the unpacking of the formulas was uncoupled from the generation of the JAGS code.
- The new architecture was incredibly easy to extend and then things went really fast. In particular, I looked a lot to brms for inspiration on the API. I wanted to make sure that the design could accommodate virtually endless oportunities.
- Here we are!
mcp was the first name I came up with. I like it because it means multiple things to me:
- Multiple Change Points as in “more than one change point”
- Multiple Change Points as in “multiple kinds of models and change points”.
- MC for “Markov Chains” to hint at the underlying MCMC sampling. We could give the package the pet name “Markov Chainge Points”.
I think I would have renamed it to “rcp” or “cpr” (“Regression with change points”) if they weren’t taken. “changepoint” is also a great name that’s already taken.