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Code to impute and analyse sports volunteering rates in English local areas using ActiveLives dataset
Implements conditional false-discovery rate hypothesis testing, used to perform high-dimensional hypothesis tests. Requires a set of summary statistics in an experiment of interest, and 'levers' on a second set of summary statistics at the same variables for a second experiment. Enables frequentist hypothesis testing with overall strong control of false-discovery rate. Can use several estimators of the cFDR function.
This repository contains R functions to compute the 'Conditional False Discovery Rate', a method to analyse genome-wide association studies (GWAS) for related diseases originally proposed by Andreasson et al (2013), in the case where the two GWAS have shared controls.These functions widen the scope of the existing technique and enable improved power.
Pipeline for generation of all plots and results in paper 'Accurate error control in high dimensional association testing using conditional false discovery rates' by James Liley and Chris Wallace (to be submitted)
Pipeline for generation of all plots and results in paper 'New estimators for conditional false discovery rates improve power and smoothness' by James Liley and Chris Wallace (to be submitted)
Pipeline to generate all results in manuscript 'Accurate error control in high dimensional association testing using conditional false discovery rates', by James Liley and Chris Wallace
Durham University MATH4267: Deep Learning and Artificial Intelligence
An example repository containing simple code
Supporting code for manuscript 'Model updating after interventions paradoxically introduces bias'
R package for estimation of optimal size of a 'holdout' set for planned updating of a predictive score
Pipelines to generate figures and data used in Optimal Holdout Sizing manuscript
Analysis of surgical risk in pulmonary endarterectomy (PEA).
Code and data for SPARRAfairness R package on CRAN
Public repository for code used in manuscript on SPARRA version 4
Code to support manuscript. Contains simulations
A package for testing for differential genetic basis between two putative disease subtypes.
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We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
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Data-Driven Documents codes.
China tencent open source team.