The MRC Biostatistics Unit understands the importance of creating accessible software for putting new statistical methods into practice.
BSU researchers routinely develop software to implement novel statistical methods, and make this software freely-available and open source.
The Unit has a GitHub page which contains mirrors of the source code for packages that we currently maintain. This also includes code to reproduce analyses in published papers.
We have developed an array of Shiny applications, which can be browsed here index of MRC BSU Shiny Applications.
The BUGS Project
BUGS is a language and various software packages for Bayesian inference Using Gibbs Sampling, conceived and initially developed at the BSU. Throughout its life span of over 30 years, BUGS has been highly influential in enabling the routine use of Bayesian methods in many areas.
For more information on how to download BUGS and WinBUGS software, go to our BUGS Project page.
R packages
A large amount of the work at the BSU is done with the R statistical software. We have developed many R packages that are widely used, including
CRAN:
- msm: multi-state modelling of intermittently-observed data
- coloc: Colocalisation tests of two genetic traits.
- MendelianRandomization: methods for performing Mendelian randomization analyses with summarized data.
- BASiCS: Bayesian Analysis of Single-Cell Sequencing data.
- GUESSFM: R package for fine mapping genetic associations in dense or imputed GWAS genotype data
- BATSS: Defines operating characteristics of Bayesian Adaptive Trials considering a generalised linear model response via Monte Carlo simulations of Bayesian GLM fitted via integrated Laplace approximations (INLA).
- MAMS: Designing multi-arm multi-stage studies with (asymptotically) normal endpoints and known variance.
- flexsurv: Flexible parametric models for time-to-event data, including the Royston-Parmar spline model, generalized gamma and generalized F distributions.
- DGP4LCF: Functionalities for analyzing high-dimensional and longitudinal biomarker data to facilitate precision medicine, using a joint model of Bayesian sparse factor analysis and dependent Gaussian processes
BioConductor:
- OnlineFDR: This package allows users to control the false discovery rate (FDR) or familywise error rate (FWER) for online multiple hypothesis testing, where hypotheses arrive in a stream.
Python packages
- vbvarsel - a variable bound, variable selection clustering algorithm.