Rd file 'EPSGO.Rd':
  \examples lines wider than 100 characters:
                             # computation intensive; for demostration reasons only for the first 100 features 
                             system.time(fit<-EPSGO(Q.func, bounds=bounds, parms.coding="log2", fminlower=0, 
                                                                              show='none', N=21,  maxevals=500, 
                                                                              pdf.name=NULL,  seed=seed,  
                                                                              # Q.func specific parameters:
                                                                              x.svm=t(train$x)[,1:100], y.svm=train$y, inner.val.method="cv" ... [TRUNCATED]
                                                                              cross.inner=5, maxIter=10 ))
                             print(paste("minimal 5-fold cv error:", fit$fmin, "by log2(lambda1)=", fit$xmin))

Rd file 'predict.Rd':
  \examples lines wider than 100 characters:
     #                                                                               cross.outer= 0, grid.search = "discrete",  
     #                                                                               lambda1.set=lambda1.1norm, show="none",
     #                                                                               parms.coding = "none",
     #                                                                               maxIter = 700, inner.val.method = "cv", cross.inner= 5,
     #                                                                               seed=seed, verbose=FALSE )      

Rd file 'svm.fs.Rd':
  \usage lines wider than 90 characters:
                                     # chose the search method for tuning lambda1,2: 'interval' or 'discrete'
                                     # method for the inner validation: cross validation, gacv   
  \examples lines wider than 100 characters:
                             system.time(scad.fix<- svm.fs(t(train$x)[,1:100], y=train$y, fs.method="scad", 
                                                                                                     cross.outer= 0, grid.search = "discrete ... [TRUNCATED]
                                                                                                     lambda1.set=lambda1.scad,
                                                                                                     parms.coding = "none", show="none",
                                                                                                     maxIter = 10, inner.val.method = "cv",  ... [TRUNCATED]
                                                                                                     seed=seed, verbose=FALSE)       )
                     #epsi.set<-vector(); for (num in (1:9)) epsi.set<-sort(c(epsi.set, c(num*10^seq(-5, -1, 1 ))) )
                     #                                                                               cross.outer= 0, grid.search = "discrete ... [TRUNCATED]
                     #                                                                               lambda1.set=lambda1.1norm,
                     #                                                                               parms.coding = "none", show="none",
                     #                                                                               maxIter = 700, inner.val.method = "cv", ... [TRUNCATED]
                     #                                                                               seed=seed, verbose=FALSE )      
                             system.time( scad<- svm.fs(t(train$x)[,1:100], y=train$y, fs.method="scad", bounds=bounds, 
                                                                                                     cross.outer= 0, grid.search = "interval ... [TRUNCATED]
                                                                                                     inner.val.method = "cv", cross.inner= 5 ... [TRUNCATED]
                                                                                                      seed=seed, parms.coding = "log2", show ... [TRUNCATED]

These lines will be truncated in the PDF manual.