remove file

DESCRIPTION

 Package: cornet
 Version: 0.0.5
 Title: Elastic Net with Dichotomised Outcomes
-Description: Implements lasso and ridge regression for dichotomised outcomes (Rauschenberger et al. 2019). Such outcomes are not naturally but artificially binary. They indicate whether an underlying measurement is greater than a threshold.
+Description: Implements lasso and ridge regression for dichotomised outcomes (Rauschenberger et al. 2021). Such outcomes are not naturally but artificially binary. They indicate whether an underlying measurement is greater than a threshold.
 Depends: R (>= 3.0.0)
 Imports: glmnet, palasso
 Suggests: knitr, testthat, rmarkdown
@@ -9,7 +9,7 @@ Enhances: RColorBrewer, MASS, mvtnorm
 Authors@R: person("Armin","Rauschenberger",email="armin.rauschenberger@uni.lu",role=c("aut","cre"))
 VignetteBuilder: knitr
 License: GPL-3
-LazyData: true
+LazyData: false
 Language: en-GB
 RoxygenNote: 7.1.1
 URL: https://github.com/rauschenberger/cornet

docs/articles/vignette.html

@@ -131,7 +131,7 @@
 <div id="references" class="section level1">
 <h1 class="hasAnchor">
 <a href="#references" class="anchor"></a>References</h1>
-<p>Rauschenberger A, and Glaab E (2020). “Predicting artificial binary outcomes from high-dimensional data”. <em>Manuscript in preparation.</em></p>
+<p>Rauschenberger A, and Glaab E (2021). “Predicting artificial binary outcomes from high-dimensional data”. <em>Manuscript in preparation.</em></p>
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docs/index.html

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 <script src="pkgdown.js"></script><meta property="og:title" content="Elastic Net with Dichotomised Outcomes">
-<meta property="og:description" content="Implements lasso and ridge regression for dichotomised outcomes (Rauschenberger et al. 2019). Such outcomes are not naturally but artificially binary. They indicate whether an underlying measurement is greater than a threshold.">
+<meta property="og:description" content="Implements lasso and ridge regression for dichotomised outcomes (Rauschenberger et al. 2021). Such outcomes are not naturally but artificially binary. They indicate whether an underlying measurement is greater than a threshold.">
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docs/pkgdown.yml

@@ -6,5 +6,5 @@ articles:
   article: article.html
   simulation: simulation.html
   vignette: vignette.html
-last_built: 2021-04-19T07:10Z
+last_built: 2021-04-19T07:49Z
 

vignettes/vignette.Rmd

@@ -79,7 +79,7 @@ Here we observe that combined regression outperforms logistic regression (lower
 
 # References
 
-Rauschenberger A, and Glaab E (2020). "Predicting artificial binary outcomes from high-dimensional data". *Manuscript in preparation.*
+Rauschenberger A, and Glaab E (2021). "Predicting artificial binary outcomes from high-dimensional data". *Manuscript in preparation.*
 
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