Making Shiny shine brighter AND Bayesian modeling AND wraps, snacks, drinks

# Making Shiny shine brighter
Olga Mierzwa-Sulima

# Bayesian modeling in Stan for personality test data
Morgan Ström

We will serve wraps, drinks, snacks.

# Making Shiny shine brighter
Olga Mierzwa-Sulima
There is no need to praise Shiny for its influence on presenting results. As with many other technology stacks, Shiny could benefit from community contributions for further development of the package itself and the growth of independent packages that add new features.

In this presentation we will show six packages that add interesting capabilities to Shiny such as beautiful UI `shiny.semantic` and `semantic.dashboard`, routing `shiny.router`, authentication `shiny.users`, app usage monitoring `shiny.admin` and internationalization `shiny.i18n`.

We will demo the apps using the packages. We show how their usage improves working with Shiny and what their development adds to the open source community. Presented packages help companies adopt R/shiny and proves to be very useful based on the feedback for out clients.

Olga is a senior data scientist at Appsilon Data Science. She leads a team of data scientists and build data science predictive/explanatory solutions and deploy them in production, usually wrapped in a Shiny App UI.

She contributes Appsilon’s open-source R packages. Olga holds a MSc degree in Econometrics from the University of Rotterdam.
She co-organizes the largest meetup of R users in Poland and is a co-founder of R-Ladies Warsaw chapter.

# Bayesian modeling in Stan for personality test data
Morgan Ström

Content:

• Intro to Item Response Theory
• Graded Response Model
• Stan impelementation & syntax
• rstan R package
• ltm R package
• Comparison of results