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://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

3.28 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: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64NOTE111
linux-devel-x86_64NOTE128
source / vignettesOK203
linux-release-arm64NOTE109
linux-release-x86_64NOTE110
macos-release-arm64NOTE102
macos-release-x86_64NOTE276
macos-oldrel-arm64FAIL77
macos-oldrel-x86_64FAIL178
windows-devel-arm64NOTE356
windows-devel-x86_64NOTE146
windows-release-arm64NOTE371
windows-release-x86_64NOTE113
windows-oldrel-x86_64FAIL47
wasm-releaseOK118

Exports:PMR_individualPMR_summary

Dependencies:RcppRcppArmadillo