ovarian -> cancer in survival

Author: Torsten Hothorn

DESCRIPTION

@@ -28,7 +28,7 @@ Description: Functional gradient descent algorithm
   a hands-on tutorial is available from \doi{10.1007/s00180-012-0382-5}.
   The package allows user-specified loss functions and base-learners.
 Depends: R (>= 3.2.0), methods, stats, parallel, stabs (>= 0.5-0)
-Imports: Matrix, survival, splines, lattice, nnls, quadprog, utils,
+Imports: Matrix, survival (>= 3.2-10), splines, lattice, nnls, quadprog, utils,
 	graphics, grDevices, partykit (>= 1.2-1)
 Suggests: TH.data, MASS, fields, BayesX, gbm, mlbench,
     RColorBrewer, rpart (>= 4.0-3), randomForest, nnet,

man/survFit.Rd

@@ -45,7 +45,8 @@
 \examples{
 
 library("survival")
-data("ovarian", package = "survival")
+data("cancer", package = "survival")
+
 
 fm <- Surv(futime,fustat) ~ age + resid.ds + rx + ecog.ps
 fit <- glmboost(fm, data = ovarian, family = CoxPH(),