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-4.5)PPMR_1.0.zip(r-4.4)PPMR_1.0.zip(r-4.3)
PPMR_1.0.tgz(r-4.4-x86_64)PPMR_1.0.tgz(r-4.4-arm64)PPMR_1.0.tgz(r-4.3-x86_64)PPMR_1.0.tgz(r-4.3-arm64)
PPMR_1.0.tar.gz(r-4.5-noble)PPMR_1.0.tar.gz(r-4.4-noble)
PPMR_1.0.tgz(r-4.4-emscripten)PPMR_1.0.tgz(r-4.3-emscripten)
PPMR.pdf |PPMR.html✨
PPMR/json (API)

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

Peer review:

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:

3.28 score 2 stars 19 scripts 116 downloads 1 mentions 2 exports 2 dependencies

Last updated 5 years agofrom:27e6c11167. Checks:OK: 1 NOTE: 8. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 19 2024
R-4.5-win-x86_64NOTENov 19 2024
R-4.5-linux-x86_64NOTENov 19 2024
R-4.4-win-x86_64NOTENov 19 2024
R-4.4-mac-x86_64NOTENov 19 2024
R-4.4-mac-aarch64NOTENov 19 2024
R-4.3-win-x86_64NOTENov 19 2024
R-4.3-mac-x86_64NOTENov 19 2024
R-4.3-mac-aarch64NOTENov 19 2024

Exports:PMR_individualPMR_summary

Dependencies:RcppRcppArmadillo