1. Select best model for each family based on Cross-validation with mlogL
2. Compare variable importances using traditional ways (t-statistics for regression, coefficients for glmnet, variable importances for trees, importance for gbm)
1. Added cross-validation function cv.negBino() function
2. Added best fitting functions best.poisReg.fit() and best.negBino.fit()
3. Moved varImpStandard() from some other place
Added cross validations for negative binomial and glmnetPois
Moved varImpStandard() function to some functions.R