getBeta: Beta optimization routing
In gldrm: Generalized Linear Density Ratio Models

Beta optimization routing

1 2	getBeta(x, y, spt, ySptIndex, f0, linkinv, mu.eta, offset, sampprobs, betaStart, thStart, thetaControl = theta.control(), betaControl = beta.control())

`x`	Covariate matrix.
`y`	Response vector.
`spt`	Vector of unique observed support points in the response.
`ySptIndex`	Index of each `y` value within the `spt` vector.
`f0`	Current values of f0.
`linkinv`	Inverse link function.
`mu.eta`	Deriviative of inverse link function.
`offset`	Vector of known offset values to be added to the linear combination (x' beta) for each observation. Mostly intended for likelihood ratio and score confidence intervals.
`sampprobs`	Optional matrix of sampling probabilities.
`betaStart`	Starting values for beta (typically the estimates from the previous iteration).
`thStart`	Starting theta values. Needs to be a list of values matching the output of the `getTheta` function.
`betaControl`	A "betaControl" object returned from the `beta.control` function.
`thetaConrol`	A "thetaControl" object returned from the `theta.control` function.

A list containing the following:

beta Updated values.
mu Updated mean for each observation.
th Updated list returned from the getTheta function.
llik Updated log-likelihood.
iter Number of iterations until convergence. (Will always be one unless maxiter is increased to something greater than one using the betaControl argument.)
conv Convergence indicator. (Will always be FALSE unless maxiter is increased to something greater than one using the betaControl argument.)