Package: MrDAG 0.1.3

Leonardo Bottolo

MrDAG: Bayesian Causal Graphical Model for Joint Mendelian Randomization Analysis of Multiple Exposures and Outcomes

Performs Mendelian randomization for multiple exposures and outcomes with Bayesian structure learning and genetically predicted effects estimation. The directionality of the genetically predicted effects between the exposures and the outcomes is assumed known, i.e., the exposures can only be potential causes of the outcomes and no reverse causation is allowed.

Authors:Leonardo Bottolo [aut, cre], Verena Zuber [aut, ctb]

MrDAG_0.1.3.tar.gz
MrDAG_0.1.3.zip(r-4.7-any)MrDAG_0.1.3.zip(r-4.6-any)MrDAG_0.1.3.zip(r-4.5-any)
MrDAG_0.1.3.tgz(r-4.6-any)MrDAG_0.1.3.tgz(r-4.5-any)
MrDAG_0.1.3.tar.gz(r-4.7-any)MrDAG_0.1.3.tar.gz(r-4.6-any)
MrDAG_0.1.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
MrDAG/json (API)

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

Bug tracker:https://github.com/lb664/mrdag/issues

Datasets:
  • LBT2MD_data - MrDAG data set: Lifestyle and behavioural exposures that might impact mental health phenotypes
  • MD2LBT_data - MrDAG data set: Mental health phenotypes that might impact lifestyle and behavioural traits

On CRAN:

Conda:

2.74 score 11 stars 4 exports 18 dependencies

Last updated from:336c4d46d9. Checks:9 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-develOK204
source / vignettesOK176
linux-releaseOK104
macos-releaseOK98
macos-oldrelOK107
windows-develOK57
windows-releaseOK58
windows-oldrelOK69
wasm-releaseOK93

Exports:get_causaleffectget_causaleffectsget_edgeprobMrDAG

Dependencies:clicpp11gluegRbaseigraphlatticelifecyclemagrittrMatrixmvtnormpkgconfigrbibutilsRcppRcppArmadilloRcppEigenRdpackrlangvctrs