**Statistical Modeling, Causal Inference, and Social Science » R**, and kindly contributed to R-bloggers)

This is really fun. I love how Ripley thinks, with just about every concept considered in broad generality while being connected to real-data examples. He’s a great statistical storyteller as well.

**. . . and Wickham on exploratory model analysis**

I came across Ripley’s slides in a reference from Hadley Wickham’s article on exploratory model analysis. I’ve been interested for awhile in statistical graphics for understanding fitted models (which is different than the usual use of graphics to visualize data or to understand discrepancies of data from models). Recently I’ve started using the term “exploratory model analysis,” and it seemed like such a natural phrase that I thought I’d google it and see what’s up. I found the above-linked paper by Hadley, which in turn refers to a paper by Antony Unwin, Chris Volinksy, and Sylvia Winkler that defines “exploratory modelling analysis” as “the evaluation and comparison of many models simultaneously.” That’s not exactly what I had in mind, but it’s pretty close.

P.S. I was curious to see what research Ripley’s been up to lately. His webpage doesn’t seem to have been updated in many years, but a Google scholar search revealed this article on estimating disease prevalence. I have no idea how he got involved in that project, but I hope he is getting deep enough into the problem to inspire further insights. (The search also revealed a bunch of articles and patents on electric household appliances, but that seems to be a different Brian D. Ripley.)

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