A month ago I mentioned that I’d been using a discrete event simulation for estimating transition probabilities from parametric multi-state models. I’ve now turned this code into a general package containing resources for multi-state modelling, called multistateutils (I know, I’m very imaginative) which may be of interest to other people working with multi-state models in R. The current release is available on CRAN, while the development is still on GitHub.
I’m very happy to announce the first ‘official’ release of version 1.0.0 of rprev, the R package for estimating disease prevalence by simulation. This is useful for epidemiologists who have registry data and want to know disease prevalence from time periods longer than is covered by the registry. I first released it almost exactly two years ago but had always intended to update it with the features in this release.
I’ve just returned from the 2018 Survival Analysis for Junior Researchers conference in Leiden, fresh with inspiration and wishing I was a PhD student again to have the luxury of time and independence to research all the ideas in my head. As with previous years it was extremely well organised with a variety of interesting talks, I particularly enjoyed the sessions on Causal Inference and Dynamic Prediction and hope to incorporate some of what I’ve learnt into my work.
I’ve just released an R package for estimating transition probabilities from multi-state models onto Github, found at https://github.com/stulacy/RDES. It’s not a package with a large potential audience, so I don’t intend to release it onto CRAN, rather it has a highly specific role that I developed for my own use and thought it could prove useful for someone else. Essentially, it extends the simulation functionality offered by the fantastic flexsurv package for obtaining predicted outcomes from multi-state models.
I’ve created a website for Predictaball with team ratings and match predictions for all 4 main European leagues, at thepredictaball.com. It has each team’s current rating and plots showing the change over the course of season, along with match outcome forecasts. Various statistics are also included, such as the biggest upset, worst teams in history, as well as this season’s predictive accuracy. Previously only Premiership match predictions were made available (via Twitter) and so I’m happy that I’ve finally got this website released.
This isn’t yet another blog article on why static site generators are much more suitable for powering small blogs than full dynamic sites, although I was very tempted to write it that way. Instead, it is focused on the merits of automating the build process, and providing a reference for how to set it up for Hugo sites hosted on Amazon’s S3 in the hope that I can save someone some time in the future.
I’ve just had some of my PhD research on adapting Echo State Networks (ESNs) for identifying Parkinson’s disease published. The work describes considerations to be made when applying ESNs to classification problems, with a case study of using them to differentiate between Parkinson’s Disease patients and healthy subjects based on a longitudinal positional data source. This post will briefly summarise the work, but in case you’re interested the published version is available at the publisher’s website, while I’ve uploaded a preprint here.
This post continues on from the mid-season review of the Elo system and looks at my Bayesian football prediction model, Predictaball, up to and including matchday 20 of the Premier League (29th December). I’ll go over the overall predictive accuracy and compare my model to others, including bookies, expected goals (xG), and a compilation of football models.
Overall accuracy So far, across the top 4 European leagues, there have been 696 matches with 379 (54%) of these outcomes being correctly predicted.
This is going to be the first of 2 posts looking at the mid-season performance of my football prediction and rating system, Predictaball. In this post I’m going to focus on the Elo rating system.
Premier league standings I’ll firstly look at how the teams in the Premiership stand, both in terms of their Elo rating and their accumulated points, as displayed in the table below, ordered by Elo. Over-performing teams, as defined by being at least 3 ranks higher in points than in Elo, are coloured in green, while under-performing teams, the opposite, are highlighted in red.
I’ve just released a new package onto CRAN and while it doesn’t perform any complex calculations or fit a statistical niche, it may be one of the most useful everyday libraries I’ll write. In short, epitab provides a framework for building descriptive tables by extending contingency tables with additional functionality.
I initially developed it for my work in epidemiology, as I kept coming across situations where I wanted to programmatically generate tables containing various descriptive statistics to facilitate reproducible research, but I could not find any existing software that met my requirements.