lb_auglag: Fitting Log-Binomial Models with 'auglag' from the 'alabama'...
In lb: Log-Binomial Regression

Fitting Log-Binomial Models with auglag from the alabama Package

1 2	lb_auglag(x, y, start, tol = 1e-07, ceps = 1e-07, control = list(), control.optim = list(), implementation = c("improved", "naive"))

`x`	design matrix of dimension `n * p`.
`y`	vector of observations of length `n`.
`start`	numeric (vector) starting value (of length p).
`tol`	a numeric giving the tolerance for convergence of the outer iterations (see `eps` in `control.outer` the from the `auglag` function.
`ceps`	epsilon subtracted from the right hand side (X β ≤q 0 - ceps). Since the inequality X β ≤q 0 is only fullfilled to a certain tolerance we have to substact epsilon to ensure X β ≤q 0.
`control`	passed to `control.outer`.
`control.optim`	passed to `control.optim`.
`implementation`	a character string choosing which implementation of the log-likelihood and gradient is used.