Software code
Our Mendelian randomization package for R can be obtained from CRAN at https://cran.r-project.org/web/packages/MendelianRandomization/index.html or from GitHub at https://github.com/cran/MendelianRandomization. This package implements a number of methods for Mendelian randomization using summarized data.
A GitHub-editable document containing software code for running Mendelian randomization (no longer maintained / somewhat out of date) is available here.
Guidelines for reading Mendelian randomization investigations
A printable version of the checklist guidelines for MR studies, taken from Burgess et al, Wellcome Open Research 2020, can be downloaded here.
Other software repositories
| Bayesian model averaging (MR-BMA): | https://github.com/verena-zuber/demo_AMD |
| Adaptive pleiotropy adjustment: | https://github.com/aj-grant/mrcovreg |
| Robust multivariable MR: | https://github.com/aj-grant/robust-mvmr |
| MRClust clustering method: | https://github.com/cnfoley/mrclust |
| NAvMIX clustering method: | https://github.com/aj-grant/navmix |
| Multivariable MR with measurement error: | https://github.com/aj-grant/mvmr-measurement-error |
| Focused MR: | https://github.com/ash-res/focused-MR |
| Conditional inference: | https://github.com/ash-res/con-cis-MR |
| Hypothesis prioritization colocalization (HyPrColoc): | https://github.com/jrs95/hyprcoloc |
| Non-linear Mendelian randomization: | https://github.com/jrs95/nlmr and https://github.com/amymariemason/SUMnlmr |
Web applets
| Sample size calculation with a continuous or binary outcome: | https://shiny.cnsgenomics.com/mRnd/ |
| Sample size calculation with a continuous or binary outcome: | http://sb452.shinyapps.io/power/ |
| Fieller’s theorem for confidence intervals with a single instrumental variable: | http://sb452.shinyapps.io/fieller/ |
| Likelihood-based method (with heterogeneity test) with summarized data: | http://sb452.shinyapps.io/summarized/ |
| Bias and type 1 error rates from sample overlap: | https://sb452.shinyapps.io/overlap/ |