Regression on transformed variable

Hi,

In many case, we need to transform the dependent variable before
fitting a regression equation, to make it "well-behaved" like close to
normal curve etc.

like,

f(y) = alpha + beta1 X1 + beta2 X2 + ... + epsilon

Now for prediction, R will typically calculate E[f(y)] based on the
fitted coefficients. However, in real scenario, we actually need to
find E[y].

Typically, we perform reverse transformation like on fitted E[f(y)] directly.

However, I believe that in this process, we also need to make some
additional correction for non-linearity in the f() to correctly
calculate  E[y]. Onr possible way to do it, may be using Taylors
approximation.

My question is there any R function that would directly do that based
on the shape of f()?

Thanks for your time.