Applied R Winter Edition: World's largest Shiny App & High Performance Boosting

This year's Applied R Munich Winter Edition will be hosted & sponsored by Roche (location, food & drinks).

Schedule:
07:00 pm - Doors open
07:30 pm - First talk (see below) + Discussion
08:15 pm - Short break
08:30 pm - Second talk (see below) + Discussion

There will be time for networking / food / drinks before, in between and after the talks.

Please, don't hesitate to contact us at appliedrmunich@gmail.com (or via twitter https://twitter.com/applied_r) if your company wants to host one of our next meetups or if you have an interesting talk about an R related topic.

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Talk 1: bioWARP - The largest shiny app in the world

Bio of the speaker:
Sebastian Wolf is a Biostatistician @ Roche building the largest R-Shiny App in the world and currently working on the open-source projects RTest and RSelenium for App testing

Abstract:
bioWARP (biostatistical Web-Applications and R Procedures) is a Shiny application enabling employees at Roche Diagnostics to create validated reports for regulatory authorities’ submissions.
bioWARP enables people using advanced statistical methods, who cannot program R. It builds a connection to the validated R-packages developed at Roche with an easy to use and elegant user interface. Its modular environment can host an unlimited number of such interfaces. One of its main feature is a module testing homogeneity of a production process by in-house developed equivalence tests.
bioWARP’s most important feature is the ability to move all statistical evaluations right into PDF reports. These are validated and can directly be used for submission to regulatory authorities.
bioWARP is called the “largest shiny application in the world” by us as it already consists of 16 modules/tools, has over 100.000 lines of code, >500 buttons and interaction items and is growing and growing and growing.

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Talk 2: compboost - Fast and Flexible Component-Wise Boosting Framework

Bio of the speaker:
Daniel Schalk is a PhD candidate at the working group for computational statistics at the LMU Munich. He currently works on how Federated Learning affects and can be applied in Machine Learning.

Abstract:
Component-wise boosting applies the boosting framework to statistical models, e.g., general additive models using component-wise smoothing splines. Boosting these kinds of models maintains interpretability and enables unbiased model selection in high dimensional feature spaces.
The R package compboost is an implementation of component-wise boosting written in C++ to obtain high runtime performance and full memory control. The main idea is to provide a modular class system which can be extended without editing the source code.