Registration
The workshop is limited in space so please register early to secure your spot.
Workshops
Please click on Details to see more details about the workshop.
Generalized Nonlinear Models in R
By Heather Turner
Date and Time: 24th November 2025, 9AM-5PM
Location: Haydon-Allen Building, Australian National University
The class of generalized linear models covers several methods commonly used in data analysis, including multiple linear regression, logistic regression, and log-linear models. But a linear predictor does not always capture the relationship we wish to model. Rather, a nonlinear predictor may provide a better description of the observed data, often with fewer and more interpretable parameters. This workshop introduces the wider class of generalized nonlinear models (GNMs) and their implementation via the R package gnm. The day will begin with a brief refresher of linear and generalized linear models. The extension to a nonlinear predictor will be motivated by the case of structured interaction models, such as Goodman’s Row-Column association model for contingency tables. The remainder of the day will be devoted to further applications, such as the UNIDIFF models for social mobility data, the Lee-Carter model for mortality data, and bespoke GNMs.
Requirements
Participants should
- bring a laptop with R installed, along with the
gnm,logmultandvcdExtrapackages. - have basic R knowledge (e.g. you have used R to load data, create simple visualisations, perform basic analyses and write simple functions or more specifically, you are familiar with concepts in Cookbook for R by Winston Chang)
- know basic statistics (e.g. simple linear regression, hypothesis testing, basic summary statistics and plots)
Desirable
Familiarity with:
- generalized linear models and/or
- nonlinear least squares models
would be beneficial but not essential.
Biography
Heather is an Associate Professor and EPSRC Research Software Engineering Fellow in the Statistics Department at the University of Warwick, UK. She has over 20 years of experience in the development of statistical code and software, gained through positions in academia, industry, and as a freelance consultant. In research, she has developed a portfolio of R packages for statistical modelling and collaborated on applications in sports, social science and agriculture. In her work with industry, she has specialised in applications in pharmaceutical R&D, with companies including Pfizer, Johnson & Johnson and Roche. Heather is active in community management and engagement among R users and developers. She is on the board of the R Foundation and chairs the R Contribution Working Group (fostering the community of contributors to the R project) and the R Forwards taskforce (widening the participation of under-represented groups in the R community).
Analysing Complex Survey and Subsample Data (with R)
By Thomas Lumley
Date and Time: 24th November 2025, 9AM-5PM
Location: Haydon-Allen Building, Australian National University
Modelling data from complex sampling designs such as multistage surveys or case-cohort designs used to require specialised expertise and software (and even hardware). In the modern world, commodity computers, free software, internet data distribution and the unifying ideas of weighting and influence functions mean that anyone can analyse datasets like these, and relatively little expertise is needed. This workshop is designed to provide a basis for doing your own analyses. We will cover
- the basic features of sampling: strata, clusters, sampling probabilities
- describing sampling to R
- summary statistics and graphics for survey data
- regression modelling for survey data
- calibration/raking of weights to use population or full-cohort information
- efficiency/robustness issues in weighted estimation
The workshop will alternate between presentations by me and exercises for you to work on.
Requirements
Assumes some experience using R, though we won’t need any advanced programming, and knowledge of regression modelling at an advanced undergraduate level. You will need a laptop with R, the survey package, and the svyVGAM package installed.
Biography
Thomas Lumley is a Professor in the Statistics Department at the University of Auckland and Affiliate Professor at the University of Washington. He is the developer of the ‘survey’ package for R and author of “Complex Surveys: A Guide to Analysis Using R” (Wiley, 2010). Thomas has given workshops on R or surveys in 13 different time zones.
Lost in Translation: Speaking Statistician in a Multi-Lingual World
By Peter Humburg and Eve Slavich
Note: this is a half-day workshop
Date and Time: 24th November 2025, 1.30PM-5PM
Location: National Film and Sound Archive of Australia
Effective communication is a crucial skill for working successfully with individuals from different disciplines. This is a common challenge for any applied statistician, whether working in statistical consulting, as the sole (bio)statistician in a research team, or in a commercial environment. From clarifying the project’s objectives and potential challenges to explaining statistical concepts in an easily understandable way for non-statisticians, a statistician’s ability to clearly articulate ideas often determines the success of interactions with clients and other stakeholders. This interactive workshop will explore common challenges faced by statisticians when communicating and will help you develop practical strategies to overcome these hurdles through a series of hands-on exercises designed to enhance your communication skills and improve stakeholder interactions.
Biography
Eve Slavich is a Statistical Consultant at UNSW Stats Central with 7 years experience consulting, spanning over 400 researchers and students, with a particular focus on ecology.
Peter Humburg is a Biostatistician at UNSW Stats Central. He has over ten years of experience consulting for researchers from many different fields, with a focus on medical research.
Deep Learning and Computer Vision in R: A Practical Introduction
By Patrick (Weihao) Li
Date and Time: 24th November 2025, 9AM-5PM
Location: Haydon-Allen Building, Australian National University
Deep learning has transformed how we extract insights from images, powering applications such as cell classification, disease detection in scans, and tissue segmentation. While these techniques are increasingly relevant in modern research, many applied statisticians are unfamiliar with the core concepts and tools behind them.
This hands-on workshop introduces the fundamentals of deep learning and its applications in computer vision, with a focus on accessibility for R users. We will begin with a high-level overview of deep learning and neural networks, then build intuition for the anatomy of a neural network, covering layers, activation functions, model training, and key components for image analysis such as convolutional and pooling layers. Practical applications will include:
- Image classification (e.g. distinguishing image types or categories)
- Object detection (e.g. locating objects within images)
- Image segmentation (time permitting)
All work will be done in R using the reticulate package to access Python-based deep learning tools. To streamline the experience, participants will use a pre-configured Posit Cloud workspace, accessible entirely through a web browser.
Requirements
Participants should:
- Create a free Posit Cloud account (details and project link will be provided)
- Be comfortable with basic R tasks (e.g. loading data, using functions, navigating RStudio)
- Have a working knowledge of basic statistical concepts (e.g. regression, classification, model accuracy)
Desirable
- No prior experience with deep learning, computer vision, or Python is required
- Some familiarity with matrices or linear algebra may help conceptual understanding, but is not necessary
Biography
Dr Patrick (Weihao) Li is a Postdoctoral Research Fellow at the Australian National University, where his research centers on applying machine learning and computer vision techniques to the grains industry. For his PhD, he developed computer vision models to automate the assessment of residual plots in regression diagnostics. An award-winning software developer, Patrick has published multiple R packages on CRAN and GitHub, contributing to the open-source ecosystem for data analysis and visualization.