From 7b184ae5f9a50bd1735577f5b9bf067d3d59a348 Mon Sep 17 00:00:00 2001 From: Jie Ren Date: Mon, 19 Feb 2024 22:21:30 -0500 Subject: [PATCH] update readme --- README.md | 66 +++++++++++++++++++++++-------------------------------- 1 file changed, 28 insertions(+), 38 deletions(-) diff --git a/README.md b/README.md index b61744b..8032235 100644 --- a/README.md +++ b/README.md @@ -8,13 +8,12 @@ -[![Travis build -status](https://travis-ci.org/jrhub/spinBayes.svg?branch=master)](https://travis-ci.org/jrhub/spinBayes) [![CRAN](https://www.r-pkg.org/badges/version/spinBayes)](https://cran.r-project.org/package=spinBayes) [![Codecov test coverage](https://codecov.io/gh/jrhub/spinBayes/branch/master/graph/badge.svg)](https://codecov.io/gh/jrhub/spinBayes?branch=master) [![CRAN RStudio mirror downloads](http://cranlogs.r-pkg.org/badges/spinBayes)](http://www.r-pkg.org/pkg/spinBayes) +[![R-CMD-check](https://github.com/jrhub/spinBayes/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/jrhub/spinBayes/actions/workflows/R-CMD-check.yaml) Many complex diseases are known to be affected by the interactions @@ -36,28 +35,28 @@ implemented in C++. ## Features - - BVCfit() integrates five different models for G×E Bayesian variable - selection. - - Generic functions BVSelection(), predict() and plot() make the - workflow very simple (see ‘Examples’). - - Highly efficient c++ implementation for MCMC algorithm. - - +- BVCfit() integrates five different models for G×E Bayesian variable + selection. +- Generic functions BVSelection(), predict() and plot() make the + workflow very simple (see ‘Examples’). +- Highly efficient c++ implementation for MCMC algorithm. + + ## How to install - - To install from github, run these two lines of code in R +- To install from github, run these two lines of code in R - + install.packages("devtools") devtools::install_github("jrhub/spinBayes") - - Released versions of spinBayes are available on CRAN - , - and can be installed within R via +- Released versions of spinBayes are available on CRAN + , and + can be installed within R via - + install.packages("spinBayes") @@ -69,18 +68,18 @@ implemented in C++. library(spinBayes) data(gExp.L) - + test = sample((1:nrow(X2)), floor(nrow(X2)/5)) spbayes=BVCfit(X2[-test,], Y2[-test,], Z2[-test,], E2[-test,], clin2[-test,]) spbayes - + selected = BVSelection(spbayes) selected - + pred = predict(spbayes, X2[test,], Z2[test,], E2[test,], clin2[test,], Y2[test,]) pred$pmse # c(pred$y.pred) - + ## plot the varying effects plot(spbayes) @@ -89,14 +88,14 @@ implemented in C++. #### Example.2 (non-structural) data(gExp.L) - + test = sample((1:nrow(X2)), floor(nrow(X2)/5)) spbayes=BVCfit(X2[-test,], Y2[-test,], Z2[-test,], E2[-test,], clin2[-test,], structural=FALSE) spbayes - + selected = BVSelection(spbayes) selected - + pred = predict(spbayes, X2[test,], Z2[test,], E2[test,], clin2[test,], Y2[test,]) pred$pmse # c(pred$y.pred) @@ -104,45 +103,36 @@ implemented in C++. #### Example.3 (non-sparse) data(gExp.L) - + test = sample((1:nrow(X2)), floor(nrow(X2)/5)) spbayes=BVCfit(X2[-test,], Y2[-test,], Z2[-test,], E2[-test,], clin2[-test,], structural=TRUE, sparse=FALSE) spbayes - + selected = BVSelection(spbayes) selected - + pred = predict(spbayes, X2[test,], Z2[test,], E2[test,], clin2[test,], Y2[test,]) pred$pmse # c(pred$y.pred) - - - - - - ## Methods This package provides implementation for methods proposed in - - Ren, J., Zhou, F., Li, X., Chen, Q., Zhang, H., Ma, S., Jiang, Y., - Wu, C. (2019) Semi-parametric Bayesian variable selection for - gene-environment interactions. *Statistics in Medicine* 39: 617– - 638. +- Ren, J., Zhou, F., Li, X., Chen, Q., Zhang, H., Ma, S., Jiang, Y., + Wu, C. (2019) Semi-parametric Bayesian variable selection for + gene-environment interactions. *Statistics in Medicine* 39: 617– 638. + - - -