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)PPMR_1.0.zip(r-4.6)PPMR_1.0.zip(r-4.5)
PPMR_1.0.tgz(r-4.6-x86_64)PPMR_1.0.tgz(r-4.6-arm64)PPMR_1.0.tgz(r-4.5-x86_64)PPMR_1.0.tgz(r-4.5-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://umich-biostatistics.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

3.30 score 2 stars 20 scripts 209 downloads 1 mentions 2 exports 2 dependencies

Last updated from:27e6c11167. Checks:8 NOTE, 2 OK, 3 FAIL. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64NOTE162
linux-devel-x86_64NOTE113
source / vignettesOK160
linux-release-arm64NOTE123
linux-release-x86_64NOTE113
macos-release-arm64NOTE100
macos-release-x86_64NOTE211
macos-oldrel-arm64FAIL73
macos-oldrel-x86_64FAIL129
windows-develNOTE126
windows-releaseNOTE147
windows-oldrelFAIL89
wasm-releaseOK111

Exports:PMR_individualPMR_summary

Dependencies:RcppRcppArmadillo