Package: PPMR 1.0

Michael Kleinsasser

PPMR: Probabilistic Two Sample Mendelian Randomization

Efficient statistical inference of two-sample MR (Mendelian Randomization) analysis. It can account for the correlated instruments and the horizontal pleiotropy, and can provide the accurate estimates of both causal effect and horizontal pleiotropy effect as well as the two corresponding p-values. There are two main functions in the 'PPMR' package. One is PMR_individual() for individual level data, the other is PMR_summary() for summary data.

Authors:Zhongshang Yuan [aut], Xiang Zhou [aut], Michael Kleinsasser [cre]

PPMR_1.0.tar.gz
PPMR_1.0.zip(r-4.7-x86_64)PPMR_1.0.zip(r-4.7-arm64)PPMR_1.0.zip(r-4.6-x86_64)PPMR_1.0.zip(r-4.6-arm64)
PPMR_1.0.tgz(r-4.6-x86_64)PPMR_1.0.tgz(r-4.6-arm64)
PPMR_1.0.tar.gz(r-4.7-arm64)PPMR_1.0.tar.gz(r-4.7-x86_64)PPMR_1.0.tar.gz(r-4.6-arm64)PPMR_1.0.tar.gz(r-4.6-x86_64)
PPMR_1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
PPMR/json (API)

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

Bug tracker:https://github.com/umich-biostatistics/ppmr/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:

On CRAN:

Conda:

openblascppopenmp

3.28 score 2 stars 19 scripts 221 downloads 1 mentions 2 exports 2 dependencies

Last updated from:27e6c11167. Checks:10 NOTE, 2 OK, 3 FAIL. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64NOTE112
linux-devel-x86_64NOTE115
source / vignettesOK195
linux-release-arm64NOTE108
linux-release-x86_64NOTE118
macos-release-arm64NOTE115
macos-release-x86_64NOTE256
macos-oldrel-arm64FAIL71
macos-oldrel-x86_64FAIL179
windows-devel-arm64NOTE202
windows-devel-x86_64NOTE150
windows-release-arm64NOTE176
windows-release-x86_64NOTE93
windows-oldrel-x86_64FAIL59
wasm-releaseOK107

Exports:PMR_individualPMR_summary

Dependencies:RcppRcppArmadillo