Venables Award seminar

 Statistical Computing and Visualisation section proudly presents the Venables Award seminar. 

This award is to encourage new open source software development from the Australian community with a view to support efforts to develop and share data science and statistics methodology  This years winners are  Matthew Sainsbury-Dale, Andrew Zammit-Mangion,with FRK: Fixed Rank Kriging and the runner up, Rex Parsons, Robin Blythe, Adrian Barnett, Susannna Cramb, Steven McPhail, with predictNMB

FRK is a framework for spatial/spatiotemporal modelling and prediction in which a set of basis functions is used to model the underlying (latent) process of interest. The fixed-rank basis-function representation facilitates the modelling of big data, and the method naturally allows for non-stationary, anisotropic covariance functions. Discretisation of the spatial domain into so-called basic areal units (BAUs) facilitates the integration of observations with varying support (i.e., both point-referenced and areal supports, potentially simultaneously), and prediction over arbitrary user-specified regions. ‘FRK’ also supports inference over various manifolds, including the 2D plane and 3D sphere, and it provides helper functions to model, fit, predict, and plot with relative ease. Version 2.0.0 and above also supports the modelling of non-Gaussian data (e.g., Poisson, binomial, negative-binomial, gamma, and inverse-Gaussian) by employing a generalised linear mixed model (GLMM) framework. Zammit-Mangion and Cressie describe ‘FRK’ in a Gaussian setting, and detail its use of basis functions and BAUs, while Sainsbury-Dale et al. describe ‘FRK’ in a non-Gaussian setting; two vignettes are available that summarise these papers and provide additional examples.

predictNMB is a tool to evaluate (hypothetical) clinical prediction models based on their estimated Net Monetary Benefit (NMB). It may be used by prediction model developers who intend to find out how performant their model needs to be clinically useful or by those in health services deciding whether or not to implement an existing model.