Extracts Fisher information matrix of \(\Theta\) from objects returned by `PLN`

and its variants. Fisher matrix is computed using one of two approximation scheme: wald (default, conservative, gives large confidence interval) or louis (anticonservative). Note that the Fisher information matrix is the full-data version (scaled by the number of observations), usually noted $$I_n(\theta)$$.

```
fisher(object, type)
# S3 method for PLNfit
fisher(object, type = c("wald", "louis"))
```

- object
an R6 object with class PLNfit

- type
Either

`wald`

(default) or`louis`

. Approximation scheme used to compute the Fisher information matrix

A block-diagonal matrix with p (number of species) blocks of size d (number of covariates), assuming \(\Theta\) is a matrix of size d * p.

`fisher(PLNfit)`

: Fisher information matrix for PLNfit

`standard_error`

for standard errors