Hi all,

  I am very new to R (only two days of studies). I know a little bit of
statistical learning and looking for an implementation of CART and random
forest and therefore I am now studying R.
  I tested with rpart and randomForest package, they are quite good.
However, I need a classification tree and for each training data, there is
different loss. (i.e. the loss function is not purely 0-1, say, c_i > 0 for
each training data)
  I read the document of randomForest and found that there are no such
parameter, and then I move to rpart package to check if I can do it and
perform the bagging process by myself.

  I found that there is a "weights" parameter for rpart. Is it the one that
I can use and it will ultimately modify the Gini / Cross-entropy
calculation during the learning process?
  If it is yes, and I want to do the bagging process by myself without
referencing to randomForest package, is there any meta-algorithm package
available for this purpose?

  Thank you very much.

Best regards,
WONG Hang.
P.S. I have tested party package also. But it is not quite suitable for me
and I face some errors on it.

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