Rd file 'allan-package.Rd':
  \examples lines wider than 100 characters:
     bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=smallchunk,weights=~cont0)

Rd file 'allanVarSelect.Rd':
  \usage lines wider than 90 characters:
     allanVarSelect(BaseModel, TrnDataSetFile, ValDataSetFile, ResponseCol = 1, NumOfSteps = 10, criteria = "AIC", currentchunksize = -1, si ... [TRUNCATED]
  \examples lines wider than 100 characters:
     bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)

Rd file 'fitvbiglm.Rd':
  \usage lines wider than 90 characters:
     fitvbiglm(BaseModel, filename, currentchunksize = -1, silent = TRUE, MemoryAllowed = 0.5, TestedRows = 1000, AdjFactor = 0.095)
  \examples lines wider than 100 characters:
     bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)

Rd file 'getbestchunksize.Rd':
  \usage lines wider than 90 characters:
     getbestchunksize(filename, MemoryAllowed = 0.5, TestedRows = 1000, AdjFactor = 0.095, silent = TRUE)
  \examples lines wider than 100 characters:
             #This is done by reading in a number of rows(1000 by default)and then measuring the size of the memory
             #used.  Memory allwed is specified in Gb.  The adjfactor is a factor used to adjust memory for overhead

Rd file 'predictvbiglm.Rd':
  \usage lines wider than 90 characters:
     predictvbiglm(BaseModel, ValFileName, currentchunksize = -1, ResponseCol = 1, silent = TRUE, MemoryAllowed = 0.5, TestedRows = 1000, Ad ... [TRUNCATED]
  \examples lines wider than 100 characters:
     bigmodel <- biglm(PurePremium ~ cont1 + cont2 + cont3 + cont4 + cont5,data=firstchunk,weights=~cont0)
     predictvbiglm<-function(BaseModel,ValFileName,currentchunksize=-1,ResponseCol=1,silent=TRUE,MemoryAllowed=0.5,TestedRows=1000,AdjFactor ... [TRUNCATED]
                     currentchunksize<-getbestchunksize(ValFileName,MemoryAllowed=MemoryAllowed,TestedRows=TestedRows,AdjFactor=AdjFactor,si ... [TRUNCATED]
                             weightvector<-as.vector(eval(parse(text=paste("CurrentDataSet","$",weightname,sep=""))))
                     CurrentVariance=sum(((CurrentDataSet[,ResponseCol]-CurrentMean)^2)*weightvector)/sum(weightvector)

Rd file 'readinbigdata.Rd':
  \examples lines wider than 100 characters:
             #The return value is either the next chunk of data or NULL if there is no additional data left.
             #Additionally if a reset=TRUE flag is passed, then the data stream goes back to the beginning.

These lines will be truncated in the PDF manual.