NBinomial and Hurdle broken for highly skewed data

[hofnerb]

For highly skewed data, NBinomial cannot compute the offset:

x <- rnorm(100)
y <- rnbinom(length(x), size = 2, mu = exp(x * 5))
mod <- glmboost(y ~ x, family = NBinomial())
## Error in basefit(u, m) : 
##   could not fit any base-learner in boosting iteration 1
## In addition: There were 50 or more warnings (use warnings() to see the first 50)

warnings()
## Warning messages:
## 1: In optimize(risk, interval = range(y), y = y, w = w) :
##   NA/Inf replaced by maximum positive value
## ...

Same for Hurdle:

x <- x[y > 0]
y <- y[y > 0]
mod <- glmboost(y ~ x, family = Hurdle())
## Error in basefit(u, m) : 
##   could not fit any base-learner in boosting iteration 1
## In addition: There were 50 or more warnings (use warnings() to see the first 50)

warnings()
## Warning messages:
## 1: In optimize(riskS, interval = range(y), y = y, w = w) :
##   NA/Inf replaced by maximum positive value
## ...