Nothing

```
agcv <-
function(penalty,yy,B,quantile,DD,nb,constmat)
# asymmetric cross validation
# computes the acv score for the smoothing of the regression
# score has to be minimized dependant on parameter "penalty"
# therefore a grid search can be applied to this function
# parameters:
# penalty - smoothing parameter lambda
# yy - vector of responses
# B - basis for the approximation
# p - quantile
# DD - penalization matrix
{
aa <- asyregpen.lsfit(yy, B, quantile, abs(penalty), DD, nb, constmat)
#score = aa$weight*(yy - B%*%aa$a)^2/(1-aa$diag.hat.ma)^2
#mean(score[which(is.finite(score))],na.rm=TRUE) # laws Code
# mean(aa$weight*((yy - B%*%aa$a)/(1-aa$diag.hat.ma))^2)
mean(aa$weight * (yy - B %*% aa$a)^2)/(1-(1+sum(aa$diag.hat.ma))/length(aa$diag.hat.ma))^2
# mean(aa$weight * (yy - B %*% aa$a)^2)/(1 - aa$diag.hat.ma)^2,na.rm = T)
}
```

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