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adversarial_learning

australian_amr_modelling

Research code for fitting forecasting models to time series' of antibiotic resistance prevalence data

camel-converter

Converts a string from snake case to camel case or camel case to snake case

covid_tracker

An R shiny app which plots the daily proportion of Covid-19 tests which are positive in the United Kingdom.

decisiontree.jl

Julia implementation of Decision Tree (CART) and Random Forest algorithms

elfi

ELFI - Engine for Likelihood-Free Inference

emd-demo

euscape_amr_prediction

fb_movement_range_downloads

Python package to scrape movement data from the Facebook Geoinsights platform. Contains R interface.

fpl_predictor

gnn-model-explainer

gnn explainer

hesdataextracter

R package to extract relevant time series of ILI from retrospective HES Data

kmeranalysis

python package for analysing kmers of pneumo genes to assess their association with igG affinity

myforecaster

Package to simplify running and comparing forecasts of time series' of the prevalence of antibiotic resistance in a given location.

myinla

Package to simplify building, running and comparing models with r-INLA for assessing the spatiotemporal variation in AMR.

nfds_model_local

numpyro

Probabilistic programming with NumPy powered by JAX for autograd and JIT compilation to GPU/TPU/CPU.

pbp_amr_prediction

Research code for a project aimed at improving our understanding of the genetic determinants of antibiotic resistance in Streptococcus pneumoniae

pull_facebook_data_for_good

Imitate an API for downloading data from Facebook Data For Good

randomforestinteractions

An efficient implementation of the statistical tests described in https://www.biorxiv.org/content/10.1101/353193v1.full for detecting evidence of interactions between pairs of features in a random forest model.

rate

Code for Variable Selection in Black Box Methods with RelATive cEntrality (RATE) Measures

statsforecast

Lightning ⚡️ fast forecasting with statistical and econometric models.

unitig_amr_prediction

Research code for developing graph-based methods to predict bacterial phenotype from whole genome sequence data.