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 224 downloads 1 mentions 2 exports 2 dependencies

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

TargetResultTimeFilesSyslog
linux-devel-arm64NOTE143
linux-devel-x86_64NOTE114
source / vignettesOK171
linux-release-arm64NOTE87
linux-release-x86_64NOTE106
macos-release-arm64NOTE112
macos-release-x86_64NOTE258
macos-oldrel-arm64FAIL72
macos-oldrel-x86_64FAIL147
windows-devel-arm64NOTE217
windows-devel-x86_64NOTE152
windows-release-arm64NOTE191
windows-release-x86_64NOTE114
windows-oldrel-x86_64FAIL54
wasm-releaseOK92

Exports:PMR_individualPMR_summary

Dependencies:RcppRcppArmadillo