Julia / Statistics symposium (3)

We report on progress of Julia for statistics

One of the important application areas for Julia lies in statistics. Dataset sizes have exploded, thus creating demands on high performance. In addition, there are elegant possibilities to harness the language foundations of Julia to create useful abstractions and better code reuse. In this mini-symposium, we report on some of this progress. The minisymposium will feature:

1. Julia for statistics by Ajay Shah

An overview talk with a sense of the landscape.

2. Survey.jl: a package for studying complex survey data by Iulia Dimitru, Shikhar Mishra, Ayush Patnaik

Handling complex survey data is an important problem in statistics, and the authors have made considerable progress in building an elegant Julia package that has many of the most-used capabilities.

3. CRRao.jl: A consistent API for many useful models by Sourish Das

Students and practitioners find their path into statistical modelling is eased by using the consistent framework of CRRao.jl.

4. TSFrames.jl by Chirag Anand

Time series data is ubiquitous, and can benefit from specialised functions and user abstractions. The TSFrames.jl package fills this need.

We expect a 20 minute talk by each of these 4 which adds up to 80 minutes.

This will be a simplified version of the Statistics in Julia symposium that was in Juliacon 2022 (https://www.youtube.com/watch?v=Fewunew8wU4). It reflects the areas in which good new work has happened in the past year. Of course, the material in the minisymposium will be self-contained: it will target a new viewer, it will not be produced as a diff on the previous one.