Maximum likelihood estimation for the Generalized Box-Cox transformation.

powFit(x, alpha = NA, interval = c(-5, 5), ...)

Arguments

x	Vector of random samples from target density.
alpha	Optional value of the offset parameter. `alpha = FALSE` sets `alpha = 1 - min(x)`, thereby guaranteeing that `z = x + alpha >= 1`. This or any scalar value of `alpha` finds the conditional MLE as a function of `lambda` only. `alpha = NA` finds the joint MLE over `(lambda,alpha)`.
interval	Range of `lambda` values for one dimensional optimization.
...	Additional arguments to pass to `optimize` or `optim`, for 1- or 2-parameter optimization.

Vector of length two containing the fitted and/or known values of (lambda, alpha).

The likelihood for optimization is

L(lambda, alpha | x) = prod(dnorm(z(x | lambda, alpha)) *  |dz(x | lambda, alpha) / dx|)

, where z(x | lambda, alpha) is the Box-Cox transformation.