kfold crossvalidation for hierarchical regularized regression hierr
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x 
predictor design matrix of dimension n x p 
y 
outcome vector of length n 
external 
(optional) external data design matrix of dimension p x q 
unpen 
(optional) unpenalized predictor design matrix 
family 
error distribution for outcome variable 
penalty 
specifies regularization object for x and external. See 
weights 
optional vector of observationspecific weights. Default is 1 for all observations. 
type.measure 
loss function for crossvalidation. Options include:

nfolds 
number of folds for crossvalidation. Default is 5. 
foldid 
(optional) vector that identifies userspecified fold for each observation. If NULL, folds are automatically generated. 
parallel 
use 
... 
list of additional arguments to pass to function 
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