DESCRIPTION

 Package: starnet
 Version: 0.0.1
 Title: Stacked Elastic Net
-Description: Implements stacked elastic net regression (Rauschenberger 2020). The elastic net generalises ridge and lasso regularisation (Zou 2005, <doi:10.1111/j.1467-9868.2005.00503.x>). Instead of fixing or tuning the mixing parameter alpha, we combine multiple alpha by stacked generalisation (Wolpert 1992 <doi:10.1016/S0893-6080(05)80023-1>).
+Description: Implements stacked elastic net regression (Rauschenberger 2020, <doi:10.1093/bioinformatics/btaa535>). The elastic net generalises ridge and lasso regularisation (Zou 2005, <doi:10.1111/j.1467-9868.2005.00503.x>). Instead of fixing or tuning the mixing parameter alpha, we combine multiple alpha by stacked generalisation (Wolpert 1992 <doi:10.1016/S0893-6080(05)80023-1>).
 Depends: R (>= 3.0.0)
 Imports: glmnet, survival, cornet, joinet, Matrix
 Suggests: knitr, testthat, rmarkdown

NEWS.md

-## starnet 0.0.1 (2019-11-14)
+## starnet 0.0.1 (2020-05-26)
 
-* first submission
+* submission to CRAN
\ No newline at end of file

R/functions.R

@@ -68,9 +68,11 @@
 #' A Rauschenberger, E Glaab, and MA van de Wiel (2020).
 #' "Predictive and interpretable models via the stacked elastic net".
 #' \emph{Bioinformatics}. In press.
+#' \doi{10.1093/bioinformatics/btaa535}.
+#' \email{armin.rauschenberger@uni.lu}
 #' 
 #' @details
-#' Posthoc feature selection\strong{:} consider
+#' Post hoc feature selection\strong{:} consider
 #' argument \code{nzero} in functions
 #' \code{\link{coef}} and \code{\link{predict}}.
 #' 
@@ -409,7 +411,7 @@ coef.starnet <- function(object,nzero=NULL,...){
   pool$alpha <- meta$alpha + sum(meta$beta * base$alpha)
   pool$beta <- base$beta %*% meta$beta
   
-  # posthoc selection
+  # post hoc selection
   if(!is.null(nzero)){
      eta <- as.numeric(pool$alpha + object$info$X %*% pool$beta)
      if(stats::sd(eta)==0){return(list(alpha=pool$alpha,beta=0*pool$beta))}
@@ -503,7 +505,7 @@ print.starnet <- function(x,...){
 #' @param nzero
 #' number of non-zero coefficients\strong{:}
 #' scalar/vector including positive integer(s) or \code{NA};
-#' or \code{NULL} (no posthoc feature selection)
+#' or \code{NULL} (no post hoc feature selection)
 #' 
 #' @param nfolds.ext,nfolds.int,foldid.ext,foldid.int
 #' number of folds (\code{nfolds})\strong{:}
@@ -537,8 +539,7 @@ print.starnet <- function(x,...){
 #' y <- rnorm(n=n,mean=rowSums(X[,1:20]))
 #' \dontshow{
 #' loss <- cv.starnet(y=y,X=X,nfolds.ext=2,nfolds.int=3)}
-#' \donttest{
-#' loss <- cv.starnet(y=y,X=X)}
+#' #loss <- cv.starnet(y=y,X=X)
 #' 
 cv.starnet <- function(y,X,family="gaussian",nalpha=21,alpha=NULL,nfolds.ext=10,nfolds.int=10,foldid.ext=NULL,foldid.int=NULL,type.measure="deviance",alpha.meta=1,nzero=NULL,intercept=NULL,upper.limit=NULL,unit.sum=NULL,...){
   

R/pkgname.R

@@ -23,9 +23,9 @@
 #' 
 #' @references
 #' A Rauschenberger, E Glaab, and MA van de Wiel (2020).
-#' Predictive and interpretable models via the stacked elastic net".
+#' "Predictive and interpretable models via the stacked elastic net".
 #' \emph{Bioinformatics}. In press.
-#' 
+#' \doi{10.1093/bioinformatics/btaa535}.
 #' \email{armin.rauschenberger@uni.lu}
 #'
 #' @examples

README.Rmd

@@ -40,4 +40,4 @@ devtools::install_github("rauschenberger/starnet")
 
 ## Reference
 
-A Rauschenberger, E Glaab, MA van de Wiel (2020). "Predictive and interpretable models via the stacked elastic net". *Manuscript in preparation.*
+A Rauschenberger, E Glaab, MA van de Wiel (2020). "Predictive and interpretable models via the stacked elastic net". *Bioinformatics*, btaa535. [doi: 10.1093/bioinformatics/btaa535](https://doi.org/10.1093/bioinformatics/btaa535).

README.md

@@ -33,5 +33,6 @@ devtools::install_github("rauschenberger/starnet")
 ## Reference
 
 A Rauschenberger, E Glaab, MA van de Wiel (2020). “Predictive and
-interpretable models via the stacked elastic net”. *Manuscript in
-preparation.*
+interpretable models via the stacked elastic net”. *Bioinformatics*,
+btaa535.
+[doi: 10.1093/bioinformatics/btaa535](https://doi.org/10.1093/bioinformatics/btaa535).

cran-comments.md

 
-# Notes
+Thanks for checking the package.
 
-- doi will be added after publication
+- removed dontrun
\ No newline at end of file
+- added value tags
\ No newline at end of file

docs/index.html

@@ -12,7 +12,7 @@
 <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous">
 <!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="pkgdown.css" rel="stylesheet">
 <script src="pkgdown.js"></script><meta property="og:title" content="Stacked Elastic Net">
-<meta property="og:description" content="Implements stacked elastic net regression (Rauschenberger 2020). The elastic net generalises ridge and lasso regularisation (Zou 2005, &lt;doi:10.1111/j.1467-9868.2005.00503.x&gt;). Instead of fixing or tuning the mixing parameter alpha, we combine multiple alpha by stacked generalisation (Wolpert 1992 &lt;doi:10.1016/S0893-6080(05)80023-1&gt;).">
+<meta property="og:description" content="Implements stacked elastic net regression (Rauschenberger 2020, &lt;doi:10.1093/bioinformatics/btaa535&gt;). The elastic net generalises ridge and lasso regularisation (Zou 2005, &lt;doi:10.1111/j.1467-9868.2005.00503.x&gt;). Instead of fixing or tuning the mixing parameter alpha, we combine multiple alpha by stacked generalisation (Wolpert 1992 &lt;doi:10.1016/S0893-6080(05)80023-1&gt;).">
 <!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
@@ -97,7 +97,7 @@
 <div id="reference" class="section level2">
 <h2 class="hasAnchor">
 <a href="#reference" class="anchor"></a>Reference</h2>
-<p>A Rauschenberger, E Glaab, MA van de Wiel (2020). “Predictive and interpretable models via the stacked elastic net”. <em>Manuscript in preparation.</em></p>
+<p>A Rauschenberger, E Glaab, MA van de Wiel (2020). “Predictive and interpretable models via the stacked elastic net”. <em>Bioinformatics</em>, btaa535. <a href="https://doi.org/10.1093/bioinformatics/btaa535">doi: 10.1093/bioinformatics/btaa535</a>.</p>
 </div>
 
   </div>

docs/news/index.html

@@ -123,11 +123,11 @@
       <small>Source: <a href='https://github.com/rauschenberger/starnet/blob/master/NEWS.md'><code>NEWS.md</code></a></small>
     </div>
 
-    <div id="starnet-001-2019-11-14" class="section level2">
+    <div id="starnet-001-2020-05-26" class="section level2">
 <h2 class="hasAnchor">
-<a href="#starnet-001-2019-11-14" class="anchor"></a>starnet 0.0.1 (2019-11-14)</h2>
+<a href="#starnet-001-2020-05-26" class="anchor"></a>starnet 0.0.1 (2020-05-26)</h2>
 <ul>
-<li>first submission</li>
+<li>submission to CRAN</li>
 </ul>
 </div>
   </div>

docs/pkgdown.yml

@@ -5,5 +5,5 @@ articles:
   article: article.html
   script: script.html
   starnet: starnet.html
-last_built: 2020-05-26T11:47Z
+last_built: 2020-05-26T12:41Z
 

docs/reference/cv.starnet.html

@@ -204,7 +204,7 @@ for elastic net regularisation;
       <th>nzero</th>
       <td><p>number of non-zero coefficients<strong>:</strong>
 scalar/vector including positive integer(s) or <code>NA</code>;
-or <code>NULL</code> (no posthoc feature selection)</p></td>
+or <code>NULL</code> (no post hoc feature selection)</p></td>
     </tr>
     <tr>
       <th>intercept</th>
@@ -256,8 +256,8 @@ with the maximum number of non-zero coefficients shown in the column name.</p>
 <span class='no'>X</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/stats/Normal.html'>rnorm</a></span>(<span class='no'>n</span>*<span class='no'>p</span>),<span class='kw'>nrow</span><span class='kw'>=</span><span class='no'>n</span>,<span class='kw'>ncol</span><span class='kw'>=</span><span class='no'>p</span>)
 <span class='no'>y</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/r/stats/Normal.html'>rnorm</a></span>(<span class='kw'>n</span><span class='kw'>=</span><span class='no'>n</span>,<span class='kw'>mean</span><span class='kw'>=</span><span class='fu'><a href='https://rdrr.io/r/base/colSums.html'>rowSums</a></span>(<span class='no'>X</span>[,<span class='fl'>1</span>:<span class='fl'>20</span>]))
 <span class='co'># \dontshow{</span>
-<span class='no'>loss</span> <span class='kw'>&lt;-</span> <span class='fu'>cv.starnet</span>(<span class='kw'>y</span><span class='kw'>=</span><span class='no'>y</span>,<span class='kw'>X</span><span class='kw'>=</span><span class='no'>X</span>,<span class='kw'>nfolds.ext</span><span class='kw'>=</span><span class='fl'>2</span>,<span class='kw'>nfolds.int</span><span class='kw'>=</span><span class='fl'>3</span>)<span class='co'># }</span></div><div class='img'><img src='cv.starnet-1.png' alt='' width='700' height='433' /></div><div class='input'><span class='co'># \donttest{</span>
-<span class='no'>loss</span> <span class='kw'>&lt;-</span> <span class='fu'>cv.starnet</span>(<span class='kw'>y</span><span class='kw'>=</span><span class='no'>y</span>,<span class='kw'>X</span><span class='kw'>=</span><span class='no'>X</span>)<span class='co'># }</span></div><div class='img'><img src='cv.starnet-2.png' alt='' width='700' height='433' /></div><div class='input'>
+<span class='no'>loss</span> <span class='kw'>&lt;-</span> <span class='fu'>cv.starnet</span>(<span class='kw'>y</span><span class='kw'>=</span><span class='no'>y</span>,<span class='kw'>X</span><span class='kw'>=</span><span class='no'>X</span>,<span class='kw'>nfolds.ext</span><span class='kw'>=</span><span class='fl'>2</span>,<span class='kw'>nfolds.int</span><span class='kw'>=</span><span class='fl'>3</span>)<span class='co'># }</span></div><div class='img'><img src='cv.starnet-1.png' alt='' width='700' height='433' /></div><div class='input'>#loss &lt;- cv.starnet(y=y,X=X)
+
 </div></pre>
   </div>
   <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">

docs/reference/starnet-package.html

@@ -148,9 +148,11 @@ to open the vignette.</p>
     <h2 class="hasAnchor" id="references"><a class="anchor" href="#references"></a>References</h2>
 
     <p>A Rauschenberger, E Glaab, and MA van de Wiel (2020).
-Predictive and interpretable models via the stacked elastic net".
-<em>Bioinformatics</em>. In press.</p>
-<p><a href='mailto:armin.rauschenberger@uni.lu'>armin.rauschenberger@uni.lu</a></p>
+"Predictive and interpretable models via the stacked elastic net".
+<em>Bioinformatics</em>. In press.
+doi: <a href='https://doi.org/10.1093/bioinformatics/btaa535'>10.1093/bioinformatics/btaa535</a>
+.
+<a href='mailto:armin.rauschenberger@uni.lu'>armin.rauschenberger@uni.lu</a></p>
 
     <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
     <pre class="examples"><div class='input'><span class='co'>#--- data simulation ---</span>

docs/reference/starnet.html

@@ -227,14 +227,17 @@ contain <code><a href='https://rdrr.io/pkg/glmnet/man/cv.glmnet.html'>cv.glmnet<
 for the base and meta learners, respectively.</p>
     <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
 
-    <p>Posthoc feature selection<strong>:</strong> consider
+    <p>Post hoc feature selection<strong>:</strong> consider
 argument <code>nzero</code> in functions
 <code><a href='https://rdrr.io/r/stats/coef.html'>coef</a></code> and <code><a href='https://rdrr.io/r/stats/predict.html'>predict</a></code>.</p>
     <h2 class="hasAnchor" id="references"><a class="anchor" href="#references"></a>References</h2>
 
     <p>A Rauschenberger, E Glaab, and MA van de Wiel (2020).
 "Predictive and interpretable models via the stacked elastic net".
-<em>Bioinformatics</em>. In press.</p>
+<em>Bioinformatics</em>. In press.
+doi: <a href='https://doi.org/10.1093/bioinformatics/btaa535'>10.1093/bioinformatics/btaa535</a>
+.
+<a href='mailto:armin.rauschenberger@uni.lu'>armin.rauschenberger@uni.lu</a></p>
 
     <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
     <pre class="examples"><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/Random.html'>set.seed</a></span>(<span class='fl'>1</span>)

man/cv.starnet.Rd

@@ -59,7 +59,7 @@ for elastic net regularisation;
 
 \item{nzero}{number of non-zero coefficients\strong{:}
 scalar/vector including positive integer(s) or \code{NA};
-or \code{NULL} (no posthoc feature selection)}
+or \code{NULL} (no post hoc feature selection)}
 
 \item{intercept}{settings for meta-learner\strong{:} logical,
 or \code{NULL}
@@ -104,7 +104,6 @@ X <- matrix(rnorm(n*p),nrow=n,ncol=p)
 y <- rnorm(n=n,mean=rowSums(X[,1:20]))
 \dontshow{
 loss <- cv.starnet(y=y,X=X,nfolds.ext=2,nfolds.int=3)}
-\donttest{
-loss <- cv.starnet(y=y,X=X)}
+#loss <- cv.starnet(y=y,X=X)
 
 }

man/starnet-package.Rd

@@ -50,9 +50,9 @@ loss <- cv.starnet(y=y,X=X)}
 }
 \references{
 A Rauschenberger, E Glaab, and MA van de Wiel (2020).
-Predictive and interpretable models via the stacked elastic net".
+"Predictive and interpretable models via the stacked elastic net".
 \emph{Bioinformatics}. In press.
-
+\doi{10.1093/bioinformatics/btaa535}.
 \email{armin.rauschenberger@uni.lu}
 }
 \keyword{documentation}

man/starnet.Rd

@@ -76,7 +76,7 @@ for the base and meta learners, respectively.
 Implements stacked elastic net regression.
 }
 \details{
-Posthoc feature selection\strong{:} consider
+Post hoc feature selection\strong{:} consider
 argument \code{nzero} in functions
 \code{\link{coef}} and \code{\link{predict}}.
 }
@@ -92,4 +92,6 @@ object <- starnet(y=y,X=X,family="gaussian")
 A Rauschenberger, E Glaab, and MA van de Wiel (2020).
 "Predictive and interpretable models via the stacked elastic net".
 \emph{Bioinformatics}. In press.
+\doi{10.1093/bioinformatics/btaa535}.
+\email{armin.rauschenberger@uni.lu}
 }