Is there anyway that I can perform LASSO with Negative Binomial Regression on R?

I am performing a negative binomial regression on my dataset because the data are too dispersed to impose poisson regression. Meanwhile, I am also facing some multicollinearity problem. I already tried using `glmnet`

with `family = poisson`

, but the data is not fitting very well (for both alpha = 0 and alpha = 1).

EDIT: here is variance-covariance table of the negative binomial fit

` 8.392729e+18 1.239178e+06 -3.624090e+05 1.896258e+17 -3.702521e+17 1.239178e+06 1.119052e-04 5.201989e-06 -1.877590e+05 -2.558095e+05 -3.624090e+05 5.201989e-06 5.179343e-06 -8.021543e+04 -1.436381e+05 1.896258e+17 -1.877590e+05 -8.021543e+04 2.193290e+17 6.413947e+16 -3.702521e+17 -2.558095e+05 -1.436381e+05 6.413947e+16 2.142183e+17 `

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#### Best Answer

LASSO and other penalized methods for negative binomial and zero-inflated negative binomial are provided by the `mpath`

package in R, as has been noted on a more recent Cross Validated page. One answer on that page, however, indicates some difficulty in using `mpath`

. A recent publication illustrates an application of the `mpath`

package; a vignette in the R package reproduces the data analysis of that publication.