I’m currently running some simulations that involve running logrank tests over and over. One analysis requires 1000 runs of the logrank test.

Purpose In the last few posts, we have built a rather sophisticated linear model using Stan. This model investigates the patterns in flight departure data over the course of a year.

Purpose In the previous post, we took advantage of the rich structure of the Greenville flight data, especially the notions that the number of flights could depend on the weekday and the season of the flights.

Introduction In previous posts on Stan, we examined a dataset of flight departures from GSP international airport. We fit and interpreted a very simple model (simple mean plus random variation).

Purpose The purpose of this post is to introduce you to analyzing Stan output. We started doing this in the last post by printing out the results of the fit object returned by the stan() function.

Introduction and purpose This is a new series on Bayesian analysis using Stan, and, specifically, the R interface to Stan. Even if you don’t want to use the R interface to Stan, much of the actual Stan code may still apply to you, but for now you are on your own getting data into and back out.

Like many others, I’ve had an emotional rollercoaster of a year in 2017. Bob Rudis has published a quantified self kind of retrospection, but, alas, I haven’t really participated in Stack Overflow, Twitter, or even blogging to do something similar.

Introduction Holiday season is upon us, and many of us fly to see family. Taking flights is also a huge headache, especially with the large crowds in airports, frustrated people, and delays.