# mages' blog

## R in Insurance: Abstract submission closes end of March

Hurry! The abstract submission deadline for the 4th R in Insurance conference in London, 11 July 2016 is approaching soon.

You have until the 28th of March to submit a one-page abstract for consideration. Both academic and practitioner proposals related to R are encouraged. Please email your abstract of no more than 300 words (in text or pdf format) to rinsuranceconference@gmail.com.

Invited talks will be given by:
Details about the registration and abstract submission are given on the dedicated R in Insurance page at Cass Business School, London.

Attendance of the whole conference is the equivalent of 6.5 hours of CPD for members of the Actuarial Profession.

The organisers gratefully acknowledge the sponsorship of Verisk/ISO, Mirai Solutions, RStudio, Applied AI, CYBAEA, and OASIS Loss Modelling Framework.

## Notes from the Kölner R meeting, 26 February 2016

Last Friday the Cologne R user group came together for the 17th time. This time, we were in for a special treatment, with two talks by psychologists!

But, there was nothing to fear, we were in safe hands, and for the first time, we met at the new Microsoft office in Cologne.

 Lecture room at Microsoft, Cologne

First up was Meik Michalke from the University of Düsseldorf presenting the RKWard project. RKWard is a graphical user interface and integrated development environment for statistical analysis with R. RKWard is a fully featured and extendable environment for R, available on all platforms. Furthermore, as Meik demonstrated, it is very straightforward to build new plugins for RKWard. These plugins can extend the user interface, which is great if you build tools for people who are less familiar with R, but perhaps more with SPSS. Meik is one of the developers of RKWard and he uses it to run an analysis, develop packages and to teach statistics.

Next up was Paul-Christian Bürkner from the University of Münster, presenting an overview of his brms package. The name is short for Bayesian regression models with Stan. Although the package is still less than one-year-old, it is already quite mature, allowing the user to specify regression models in the usual R formula syntax. brms takes those formula calls, writes out the Stan code, compiles and runs the model, and it also provides methods to plot and predict brms models. Hence, it is a great way to get started with Stan and to build more complex Bayesian models.