url -> doi; speedup

Author: Torsten Hothorn

man/Family.Rd

@@ -309,7 +309,7 @@ distribution approximation used in Ma and Huang (2005).
     Benjamin Hofner, Andreas Mayr, Nikolay Robinzonov and Matthias Schmid
     (2014). Model-based Boosting in R: A Hands-on Tutorial Using the R
     Package mboost. \emph{Computational Statistics}, \bold{29}, 3--35.\cr
-    \url{http://dx.doi.org/10.1007/s00180-012-0382-5}
+    \doi{10.1007/s00180-012-0382-5}
 
     Available as vignette via: \code{vignette(package = "mboost", "mboost_tutorial")}
 
@@ -320,7 +320,7 @@ distribution approximation used in Ma and Huang (2005).
 
     Weinhold, L., S. Pechlivanis, S. Wahl, P. Hoffmann and M. Schmid (2016) A Statistical Model for the
     Analysis of Bounded Response Variables in DNA Methylation Studies.
-    \emph{BMC Bioinformatics}. 2016; 17: 480. \url{http://dx.doi.org/10.1186/s12859-016-1347-4}
+    \emph{BMC Bioinformatics}. 2016; 17: 480. \doi{10.1186/s12859-016-1347-4}
 }
 \seealso{\code{\link{mboost}} for the usage of \code{Family}s. See
     \code{\link{boost_family-class}} for objects resulting from a call to \code{Family}. }

man/baselearners.Rd

@@ -620,16 +620,20 @@ bl1 \%O\% bl2
                bspatial(x1, x2, knots = 12, center = TRUE, df = 1)
 
   mod1 <- gamboost(form1)
+  \dontrun{
   plot(mod1)
+  }
 
   mod2 <- gamboost(form2)
   ## automated plot function:
   plot(mod2)
   ## plot sum of linear and smooth effects:
-  library(lattice)
+  library("lattice")
   df <- expand.grid(x1 = unique(x1), x2 = unique(x2))
   df$pred <- predict(mod2, newdata = df)
+  \dontrun{
   levelplot(pred ~ x1 * x2, data = df)
+  }
 
   ## specify radial basis function base-learner for spatial effect
   ## and use data-adaptive effective range (theta = NULL, see 'args')
@@ -639,13 +643,17 @@ bl1 \%O\% bl2
   form4 <- y ~ brad(x1, x2, knots = 50, args = list(theta = 0.4))
 
   mod3 <- gamboost(form3)
+  \dontrun{
   plot(mod3)
+  }
   dim(extract(mod3, what = "design", which = "brad")[[1]])
   knots <- attr(extract(mod3, what = "design", which = "brad")[[1]], "knots")
 
   mod4 <- gamboost(form4)
   dim(extract(mod4, what = "design", which = "brad")[[1]])
+  \dontrun{
   plot(mod4)
+  }
 
   ### random intercept
   id <- factor(rep(1:10, each = 5))
@@ -696,8 +704,10 @@ bl1 \%O\% bl2
   for (i in seq(along = levels(z)))
       with(nd[nd$z == i,], lines(x, pred_gam, col = z, lty = "dashed"))
   ### convenience function for plotting
+  \dontrun{
   par(mfrow = c(1,3))
   plot(mod_gam)
+  }
 
 
   ### remove intercept from base-learner

man/cvrisk.Rd

@@ -105,7 +105,7 @@ cv(weights, type = c("bootstrap", "kfold", "subsampling"),
   importance of knowing when to stop - a sequential stopping rule for
   component-wise gradient boosting. \emph{Methods of Information in
     Medicine}, \bold{51}, 178--186. \cr
-  DOI: \url{http://dx.doi.org/10.3414/ME11-02-0030}
+  DOI: \doi{10.3414/ME11-02-0030}
 
 }
 \seealso{\code{\link{AIC.mboost}} for

man/gamboost.Rd

@@ -107,7 +107,7 @@ gamboost(formula, data = list(), na.action = na.omit, weights = NULL,
   Benjamin Hofner, Andreas Mayr, Nikolay Robinzonov and Matthias Schmid
   (2014). Model-based Boosting in R: A Hands-on Tutorial Using the R
   Package mboost. \emph{Computational Statistics}, \bold{29}, 3--35.\cr
-  \url{http://dx.doi.org/10.1007/s00180-012-0382-5}
+  \doi{10.1007/s00180-012-0382-5}
 
   Available as vignette via: \code{vignette(package = "mboost", "mboost_tutorial")}
 }

man/glmboost.Rd

@@ -80,7 +80,7 @@
   Benjamin Hofner, Andreas Mayr, Nikolay Robinzonov and Matthias Schmid
   (2014). Model-based Boosting in R: A Hands-on Tutorial Using the R
   Package mboost. \emph{Computational Statistics}, \bold{29}, 3--35.\cr
-  \url{http://dx.doi.org/10.1007/s00180-012-0382-5}
+  \doi{10.1007/s00180-012-0382-5}
 
   Available as vignette via: \code{vignette(package = "mboost", "mboost_tutorial")}
 }

man/mboost_fit.Rd

@@ -93,7 +93,7 @@ mboost_fit(blg, response, weights = rep(1, NROW(response)), offset = NULL,
   Benjamin Hofner, Andreas Mayr, Nikolay Robinzonov and Matthias Schmid
   (2014). Model-based Boosting in R: A Hands-on Tutorial Using the R
   Package mboost. \emph{Computational Statistics}, \bold{29}, 3--35.\cr
-  \url{http://dx.doi.org/10.1007/s00180-012-0382-5}
+  \doi{10.1007/s00180-012-0382-5}
 
   Available as vignette via: \code{vignette(package = "mboost", "mboost_tutorial")}
 

man/methods.Rd

@@ -325,7 +325,7 @@ downstream.test(object, ...)
   Benjamin Hofner, Andreas Mayr, Nikolay Robinzonov and Matthias Schmid
   (2014). Model-based Boosting in R: A Hands-on Tutorial Using the R
   Package mboost. \emph{Computational Statistics}, \bold{29}, 3--35.\cr
-  \url{http://dx.doi.org/10.1007/s00180-012-0382-5}
+  \doi{10.1007/s00180-012-0382-5}
 
   Clifford M. Hurvich, Jeffrey S. Simonoff and Chih-Ling Tsai (1998),
   Smoothing parameter selection in nonparametric regression using
@@ -348,7 +348,7 @@ downstream.test(object, ...)
   importance of knowing when to stop - a sequential stopping rule for
   component-wise gradient boosting. \emph{Methods of Information in
     Medicine}, \bold{51}, 178--186. \cr
-  DOI: \url{http://dx.doi.org/10.3414/ME11-02-0030}
+  DOI: \doi{10.3414/ME11-02-0030}
 
 }
 \seealso{

man/plot.Rd

@@ -114,7 +114,7 @@
   Benjamin Hofner, Andreas Mayr, Nikolay Robinzonov and Matthias Schmid
   (2014). Model-based Boosting in R: A Hands-on Tutorial Using the R
   Package mboost. \emph{Computational Statistics}, \bold{29}, 3--35.\cr
-  \url{http://dx.doi.org/10.1007/s00180-012-0382-5}
+  \doi{10.1007/s00180-012-0382-5}
 }
 \seealso{
   \code{\link{mboost_methods}} for further methods.