input arguments are generally similar to those of glm and (g)lmer, in particular for the spaMM::fitme
function, with the exception of the prior.weights argument, which is simply weights in the other packages.
The name prior.weights seems more consistent, since e.g. glm returns its input weights as output prior.weights, while its output weights are instead the weights in the final iteration of an iteratively weighted least-square fit.
The \bolddefault likelihood target for dispersion parameters is restricted likelihood (REML estimation) for corrHLfit and (marginal) likelihood (ML estimation) for fitme.
Model fits may provide restricted likelihood values(ReL) even if restricted likelihood is is not used as an objective function at any step in the analysis.
See good-practice for advice about the proper syntax of formula.
Computation times depend on control.HLfit$conv.threshold, which is by default 1e-5. Increasing it to 1e-4 had no notable effect (08/2016) on the tests in the tests directory of the package, except reducing computation time. spaMM is therefore slower but safer than it could be.
Chambers J.M. (2008) Software for data analysis: Programming with R. Springer-Verlag New York