Hi,

I’m interested in how to implement calculation of partial derivatives/marginal effects similar to the package marginaleffects in r.

I translated some code from the excellent documentation, to simply calculate the marginal effect of hp for every unit in the dataset.

```
using GLM, RDatasets, DataFrames
mtcars = dataset("datasets", "mtcars")
rename!(mtcars, lowercase.(names(mtcars)))
lm_mod = glm(@formula(mpg ~ hp * cyl), mtcars, Normal())
partial_derivative = function(data, model, eps)
d1 = transform(data, :hp => (x -> x .- eps / 2) => :hp)
d2 = transform(data, :hp => (x -> x .+ eps / 2) => :hp)
(p1, p2) = map(d -> predict(model, d), [d1, d2])
round.((p2-p1)/eps, digits=2)
end
partial_derivative(mtcars, lm_mod, 1e-4)
```

What would be a reasonable way to implemented this in a way that could give me unit-level marginaleffects for every variable in the model efficiently?

I have been looking at ForwardDiff, but haven’t been able to figure out how to do it.

Note this is crossposted on zulip.