Software

R packages and other software developed by the group

EpiNow2: Estimate Real-Time Case Counts and Time-Varying Epidemiological Parameters.

Estimates the time-varying reproduction number, rate of spread, and doubling time using a range of open-source tools (Abbott et al. (2020)), and current best practices (Gostic et al. (2020)). It aims to help users avoid some of the limitations of naive implementations in a framework that is informed by community feedback and is actively supported.

covid19.nhs.data: Covid 19 England Hospital Admissions.

Facilitates access to data on NHS trust level COVID-19 data aggregated to a range of spatial scales.

epimixr: Epidemiological analysis using social mixing matrices.

Provides methods to conduct epidemiological analysis using social mixing matrices, such as calculating contact-adjusted immunity levels or age distributions of epidemics.

qrensemble: Forecast ensembles using Quantile Regression Average (QRA).

Performs quantile regression average

rbi: Interface to ‘LibBi’.

Provides a complete interface to ‘LibBi’, a library for Bayesian inference (see https://libbi.org and Murray, 2015 for more information). This includes functions for manipulating ‘LibBi’ models, for reading and writing ‘LibBi’ input/output files, for converting ‘LibBi’ output to provide traces for use with the coda package, and for running ‘LibBi’ to conduct inference.

rbi.helpers: ‘rbi’ Helper Functions.

Contains a collection of helper functions to use with ‘rbi’, the R interface to ‘LibBi’, described in Murray et al. (2015). It contains functions to adapt the proposal distribution and number of particles in particle Markov-Chain Monte Carlo, as well as calculating the Deviance Information Criterion (DIC) and converting between times in ‘LibBi’ results and R time/dates.

scoringutils: Utilities for Scoring and Assessing Predictions.

Facilitate the evaluation of forecasts in a convenient framework based on data.table. It allows user to to check their forecasts and diagnose issues, to visualise forecasts and missing data, to transform data before scoring, to handle missing forecasts, to aggregate scores, and to visualise the results of the evaluation. The package mostly focuses on the evaluation of probabilistic forecasts and allows evaluating several different forecast types and input formats. Find more information about the package in the Vignettes as well as in the accompanying paper,.

socialmixr: Social Mixing Matrices for Infectious Disease Modelling.

Provides methods for sampling contact matrices from diary data for use in infectious disease modelling, as discussed in Mossong et al. (2008).

stackr: Create Mixture Models From Predictive Samples.

The stackr package provides an easy way to combine predictions from individual time series or panel data models to an ensemble. stackr stacks (Yuling Yao, Aki Vehtari, Daniel Simpson, and Andrew Gelman (2018)) Models according to the Continuous Ranked Probability Score (CRPS) (Tilmann Gneiting & Adrian E Raftery (2007)) over k-step ahead predictions. It is therefore especially suited for timeseries and panel data. A function for leave-one-out CRPS may be added in the future. Predictions need to be predictive distributions represented by predictive samples. Usually, these will be sets of posterior predictive simulation draws generated by an MCMC algorithm. Given some training data with true observed values as well as predictive samples generated from different models, crps_weights finds the optimal (in the sense of minimizing expected cross-validation predictive error) weights to form an ensemble from these models. Using these weights, mixture_from_samples can then provide samples from the optimal model mixture by drawing from the predictice samples of the individual models in the correct proportion. This gives a mixture model solely based on predictive samples and is in this regard superior to other ensembling techniques like Bayesian Model Averaging.