update readme

README.md

@@ -8,13 +8,12 @@
 
 <!-- badges: start -->
 
-[![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)
 <!-- badges: end -->
 
 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. <!-- + sparse --> <!-- + VC --> <!-- + structural -->
-  - Generic functions BVSelection(), predict() and plot() make the
-    workflow very simple (see ‘Examples’).
-  - Highly efficient c++ implementation for MCMC algorithm.
-    <!-- * Testing coverage >80%  -->
-    <!-- [![Codecov test coverage](https://codecov.io/gh/jrhub/spinBayes/branch/master/graph/badge.svg)](https://codecov.io/gh/jrhub/spinBayes?branch=master) -->
+- BVCfit() integrates five different models for G×E Bayesian variable
+  selection. <!-- + sparse --> <!-- + VC --> <!-- + structural -->
+- Generic functions BVSelection(), predict() and plot() make the
+  workflow very simple (see ‘Examples’).
+- Highly efficient c++ implementation for MCMC algorithm.
+  <!-- * Testing coverage >80%  -->
+  <!-- [![Codecov test coverage](https://codecov.io/gh/jrhub/spinBayes/branch/master/graph/badge.svg)](https://codecov.io/gh/jrhub/spinBayes?branch=master) -->
 
 ## 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
 
-<!-- end list -->
+<!-- -->
 
     install.packages("devtools")
     devtools::install_github("jrhub/spinBayes")
 
-  - Released versions of spinBayes are available on CRAN
-    <!-- [(link)](https://cran.r-project.org/package=spinBayes) --> ,
-    and can be installed within R via
+- Released versions of spinBayes are available on CRAN
+  <!-- [(link)](https://cran.r-project.org/package=spinBayes) --> , and
+  can be installed within R via
 
-<!-- end list -->
+<!-- -->
 
     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)
 
@@ -89,60 +88,51 @@ 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)
 
 #### 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)
 
 <!-- ## News -->
-
 <!-- ### regnet 0.3.0 [2018-5-21] -->
-
 <!-- * Two new, easy to use, integrated interfaces: cv.regnet() and regnet(). -->
-
 <!-- * New methods for continuous and survival responses. -->
-
 <!-- * The new "clv" argument allows the presence of clinical variables that are not subject to penalty in the X matrix. -->
-
 <!-- ### regnet 0.2.0 [2017-10-14] -->
-
 <!-- * Provides c++ implementation for coordinate descent algorithms. This update significantly increases the speed of cross-validation functions in this package. -->
 
 ## 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. <https://doi.org/10.1002/sim.8434>
+- 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.
+  <https://doi.org/10.1002/sim.8434>
 
 <!-- ## References -->
-
 <!-- * Wu, C., and Ma, S. (2015). A selective review of robust variable selection with applications in bioinformatics. [Briefings in Bioinformatics, 16(5), 873–883](http://doi.org/10.1093/bib/bbu046) -->
-
 <!-- * Wu, C., Shi, X., Cui, Y. and Ma, S. (2015). A penalized robust semiparametric approach for gene-environment interactions. [Statistics in Medicine, 34 (30): 4016–4030](https://doi.org/10.1002/sim.6609) -->
-
 <!-- * Wu, C, Jiang, Y, Ren, J, Cui, Y, Ma, S. (2018). Dissecting gene-environment interactions: A penalized robust approach accounting for hierarchical structures.[Statistics in Medicine, 37:437–456](https://doi.org/10.1002/sim.7518) -->