Rd file 'superpc.cv.Rd':
  \usage lines wider than 90 characters:
     superpc.cv(fit, data, n.threshold = 20,  n.fold = NULL, folds = NULL,   n.components = 3, min.features = 5, max.features = nrow(data$x) ... [TRUNCATED]

Rd file 'superpc.fit.to.outcome.Rd':
  \usage lines wider than 90 characters:
     superpc.fit.to.outcome(fit, data.test, score, competing.predictors = NULL, print=TRUE, iter.max = 5)
  \examples lines wider than 100 characters:
     fit<- superpc.predict(a, data, data.test, threshold=1.0, n.components=1, prediction.type="continuous")

Rd file 'superpc.internal.Rd':
  \usage lines wider than 90 characters:
     superpc.xl.fit.to.clin(fit, data.test,score, pamr.xl.test.sample.labels, pamr.xl.clindata,pamr.xl.clinsamplabels, pamr.xl.clinprednames ... [TRUNCATED]
     superpc.xl.listgenes.compute(data, train.obj, fit.red, fitred.cv=NULL,num.genes=NULL, component.number = 1)
     superpc.xl.decorrelate(data, pamr.xl.train.sample.labels, pamr.xl.clindata, pamr.xl.clinsamplabels, pamr.xl.clinprednames, pamr.xl.clin ... [TRUNCATED]
     superpc.xl.decorrelate.test(object.decorr, xtest, pamr.xl.train.sample.labels, pamr.xl.clindata, pamr.xl.clinsamplabels, pamr.xl.clinpr ... [TRUNCATED]
     superpc.xl.rainbowplot(data, pred,  pamr.xl.test.sample.labels,  pamr.xl.clindata, pamr.xl.clinsamplabels, pamr.xl.clinprednames,  pamr ... [TRUNCATED]

Rd file 'superpc.listfeatures.Rd':
  \examples lines wider than 100 characters:
     fit<- superpc.predict(a, data, data.test, threshold=1.0, n.components=1, prediction.type="continuous")

Rd file 'superpc.lrtest.curv.Rd':
  \usage lines wider than 90 characters:
     superpc.lrtest.curv(object, data, newdata, n.components = 1, threshold = NULL, n.threshold = 20)
  \examples lines wider than 100 characters:
     fit<- superpc.predict(a, data, data.test, threshold=1.0, n.components=1, prediction.type="continuous")

Rd file 'superpc.plot.lrtest.Rd':
  \examples lines wider than 100 characters:
     fit<- superpc.predict(a, data, data.test, threshold=1.0, n.components=1, prediction.type="continuous")

Rd file 'superpc.plotcv.Rd':
  \usage lines wider than 90 characters:
     superpc.plotcv(object, cv.type=c("full","preval"),smooth = TRUE, smooth.df = 10, call.win.metafile=FALSE, ...)

Rd file 'superpc.plotred.lrtest.Rd':
  \examples lines wider than 100 characters:
     fit<- superpc.predict(a, data, data.test, threshold=1.0, n.components=1, prediction.type="continuous")

Rd file 'superpc.predict.Rd':
  \usage lines wider than 90 characters:
     superpc.predict(object, data, newdata, threshold, n.components = 3, prediction.type = c("continuous", "discrete", "nonzero"), n.class = ... [TRUNCATED]

Rd file 'superpc.predict.red.Rd':
  \usage lines wider than 90 characters:
     superpc.predict.red(fit, data, data.test, threshold, n.components = 3, n.shrinkage= 20, shrinkages=NULL,compute.lrtest = TRUE, sign.wt= ... [TRUNCATED]
  \examples lines wider than 100 characters:
     fit<- superpc.predict(a, data, data.test, threshold=1.0, n.components=1, prediction.type="continuous")

Rd file 'superpc.predict.red.cv.Rd':
  \examples lines wider than 100 characters:
     fit<- superpc.predict(a, data, data.test, threshold=1.0, n.components=1, prediction.type="continuous")

Rd file 'superpc.rainbowplot.Rd':
  \usage lines wider than 90 characters:
      superpc.rainbowplot(data, pred, sample.labels,  competing.predictors, call.win.metafile=FALSE)

These lines will be truncated in the PDF manual.