Package: autoMR 1.1.2

autoMR: Automated Mendelian Randomization Workflows and Visualizations

Provides tools to summarize, analyze, and visualize results from Mendelian randomization studies using summarized genetic association data. The package includes functions for generating forest plots and scatter plots at the single-nucleotide polymorphism and Mendelian randomization method levels, and for fitting multiple estimators in a unified workflow, including inverse-variance weighted estimation, Mendelian randomization Egger regression, the weighted median estimator, the robust adjusted profile score, Mendelian randomization pleiotropy residual sum and outlier, Mendelian randomization with the genotype recoding invariance property, and a Bayesian horseshoe method. Related methods are described by Burgess (2013) <doi:10.1002/gepi.21758>, Bowden (2015) <doi:10.1093/ije/dyv080>, Bowden (2016) <doi:10.1002/gepi.21965>, Zhao (2020) <doi:10.1214/19-AOS1866>, Verbanck (2018) <doi:10.1038/s41588-018-0099-7>, Dudbridge (2025) <doi:10.1371/journal.pgen.1011967>, and Grant and Burgess (2024) <doi:10.1016/j.ajhg.2023.12.002>. Related open-source software includes 'TwoSampleMR' <https://github.com/MRCIEU/TwoSampleMR>, 'mr.raps' <https://github.com/qingyuanzhao/mr.raps>, 'MR-PRESSO' <https://github.com/rondolab/MR-PRESSO>, and 'MR-Horse' <https://github.com/aj-grant/mrhorse>.

Authors:Kelin Zhong [aut, cre], Chia-Ling Kuo [aut]

autoMR_1.1.2.tar.gz
autoMR_1.1.2.zip(r-4.7)autoMR_1.1.2.zip(r-4.6)autoMR_1.1.2.zip(r-4.5)
autoMR_1.1.2.tgz(r-4.6-any)autoMR_1.1.2.tgz(r-4.5-any)
autoMR_1.1.2.tar.gz(r-4.7-any)autoMR_1.1.2.tar.gz(r-4.6-any)
autoMR_1.1.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html
card.svg |card.png
autoMR/json (API)

# Install 'autoMR' in R:
install.packages('autoMR', repos = c('https://kelinzhong.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/kelinzhong/automr/issues

Uses libs:
  • jags– Just Another Gibbs Sampler for Bayesian MCMC - binary JAGS is Just Another Gibbs Sampler. It is a program for analysis of Bayesian hierarchical models using Markov Chain Monte Carlo (MCMC) simulation not wholly unlike BUGS. JAGS was written with three aims in mind: * To have an engine for the BUGS language that runs on Unix * To be extensible, allowing users to write their own functions, distributions and samplers. * To be a plaftorm for experimentation with ideas in Bayesian modelling This package contains the 'jags' binary as well as the associated shared library modules loaded by the binary.
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

Conda:

jagscpp

3.70 score 563 downloads 9 exports 93 dependencies

Last updated from:87844dd8e7. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK187
source / vignettesOK206
linux-release-x86_64OK181
macos-release-arm64OK175
macos-oldrel-arm64OK193
windows-develOK144
windows-releaseOK126
windows-oldrelOK120
wasm-releaseOK126

Exports:export_forest_plotsexport_scatter_plotsformat_mr_inputGWAS_forestharmonize_mr_dataMR_forestplot_mr_scatterrun_mr_analysisshowplot

Dependencies:abindarrangementsaskpassbase64encbootbslibcachemclicodacodetoolscpp11crosstalkcurldata.tableDEoptimRdigestdplyrevaluatefarverfastmapfontawesomeforeachfsgenericsggplot2glmnetgluegmpgtablehighrhtmltoolshtmlwidgetshttrisobanditeratorsiterpcjquerylibjsonliteknitrlabelinglaterlatticelazyevallifecyclemagrittrMASSMatrixMatrixModelsmemoiseMendelianRandomizationmimenortestnumDerivopensslotelpillarpkgconfigplotlypromisespurrrquantregR2jagsR2WinBUGSR6rappdirsRColorBrewerRcppRcppArmadilloRcppEigenrjagsrjsonrlangrmarkdownrobustbaseS7sassscalesshapeSparseMstringistringrsurvivalsystibbletidyrtidyselecttinytexutf8vctrsviridisLitewithrxfunyaml