Package: spinBayes 0.2.1

Jie Ren

spinBayes: Semi-Parametric Gene-Environment Interaction via Bayesian Variable Selection

Many complex diseases are known to be affected by the interactions between genetic variants and environmental exposures beyond the main genetic and environmental effects. Existing Bayesian methods for gene-environment (G×E) interaction studies are challenged by the high-dimensional nature of the study and the complexity of environmental influences. We have developed a novel and powerful semi-parametric Bayesian variable selection method that can accommodate linear and nonlinear G×E interactions simultaneously (Ren et al. (2020) <doi:10.1002/sim.8434>). Furthermore, the proposed method can conduct structural identification by distinguishing nonlinear interactions from main effects only case within Bayesian framework. Spike-and-slab priors are incorporated on both individual and group level to shrink coefficients corresponding to irrelevant main and interaction effects to zero exactly. The Markov chain Monte Carlo algorithms of the proposed and alternative methods are efficiently implemented in C++.

Authors:Jie Ren, Fei Zhou, Xiaoxi Li, Cen Wu, Yu Jiang

spinBayes_0.2.1.tar.gz
spinBayes_0.2.1.tar.gz(r-4.5-noble)spinBayes_0.2.1.tar.gz(r-4.4-noble)
spinBayes_0.2.1.tgz(r-4.4-emscripten)spinBayes_0.2.1.tgz(r-4.3-emscripten)
spinBayes.pdf |spinBayes.html
spinBayes/json (API)

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

Peer review:

Bug tracker:https://github.com/jrhub/spinbayes/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:
  • E - Simulated data for demonstrating the features of BVCfit
  • E.new - Simulated data for demonstrating the features of BVCfit
  • E2 - Simulated data for demonstrating the features of BVCfit
  • X - Simulated data for demonstrating the features of BVCfit
  • X.new - Simulated data for demonstrating the features of BVCfit
  • X2 - Simulated data for demonstrating the features of BVCfit
  • Y - Simulated data for demonstrating the features of BVCfit
  • Y.new - Simulated data for demonstrating the features of BVCfit
  • Y2 - Simulated data for demonstrating the features of BVCfit
  • Z - Simulated data for demonstrating the features of BVCfit
  • Z.new - Simulated data for demonstrating the features of BVCfit
  • Z2 - Simulated data for demonstrating the features of BVCfit
  • clin - Simulated data for demonstrating the features of BVCfit
  • clin.new - Simulated data for demonstrating the features of BVCfit
  • clin2 - Simulated data for demonstrating the features of BVCfit

openblascppopenmp

2.00 score 3 scripts 132 downloads 2 exports 54 dependencies

Last updated 9 months agofrom:6a9e86df4e. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKDec 08 2024
R-4.5-linux-x86_64OKDec 08 2024

Exports:BVCfitBVSelection

Dependencies:briocallrclicodetoolscolorspacecrayondescdiffobjdigestevaluatefansifarverforeachfsggplot2glmnetgluegtableisobanditeratorsjsonlitelabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgbuildpkgconfigpkgloadpraiseprocessxpsR6RColorBrewerRcppRcppArmadilloRcppEigenrlangrprojrootscalesshapesurvivaltestthattibbleutf8vctrsviridisLitewaldowithr

Readme and manuals

Help Manual

Help pageTopics
spinBayes: Semi-Parametric Gene-Environment Interaction via Bayesian Variable SelectionspinBayes-package spinBayes
fit a Semi-parametric Bayesian variable selectionBVCfit
Variable selection for a BVCfit objectBVSelection BVSelection.BVCNonSparse BVSelection.BVCSparse
simulated data for demonstrating the features of BVCfitclin clin.new clin2 data E E.new E2 gExp.L gExp.new spbayes X X.new X2 Y Y.new Y2 Z Z.new Z2
plot a BVCfit objectplot.BVCfit
make predictions from a BVCfit objectpredict.BVCfit predict.LinOnly predict.VarLin predict.VarOnly
print a BVCfit objectprint.BVCfit
print a BVCfit.pred objectprint.BVCfit.pred
print a BVSelection objectprint.BVSelection