Speaker: Seo Young (Silvia) Kim, Ph.D. candidate in Social Sciences at Caltech (@sysilviakim)
Title: Bayesian Analysis with rstan, rstanarm, and brms
Stan is a language used for Bayesian statistical analysis, which uses Hamiltonian Monte Carlo (HMC) rather than Gibbs. It is fast, efficient, and scalable, yet can be formidable for a beginner. In this package I introduce rstan, rstanarm, and brms, all of which are R wrappers to the Stan language, and discuss the workflow of running and analyzing a Stan model via those packages.
Speaker: Jared Kai Swan
Title: Super speedy data manipulation with data.table: An overview
data.table extends R's data.frames to make working on data easy. Between its speed and its terse syntax, developers can do very complicated data analysis in very little time. Want to filter out groups in your data that don't have a certain group characteristic? That's a one-liner. Want to do an anti-join between two data sets? That's a one-liner. Want to selectively update your data based on some criteria? That's a one-liner. Oh yeah and it's also faster than dplyr. data.table is the single most impactful package that I use. Come and learn how you can use it too.
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