----- The discrepancy calculation for glmer must be wrong. All variance component estimates are being driven to zero. ----- Approximate standard errors and correlations for the fixed effects parameters in nlmer and glmer models. Should there be a general calculation of the information matrix? ----- Find out why there is a .p at the end of the model names in the printed value of anova with multiple arguments. ----- Collapse repeated grouping factors and correspondingly modify the ranef and coef methods. ----- Check the calculation of the conditional variances of the random effects. ----- Add an element to the deviance slot to hold sigma or log(sigma). The interpretation will be that an NA value means to use the conditional estimate of the scale parameter. For a generalized linear mixed model without a scale parameter set sigma = 1 (or log(sigma) = 0) and put constraints on the parameter so that it is not estimated. Upon convergence replace that element with the conditional estimate in models where it has been profiled out. ----- Consider the steps in reimplementing AGQ. First you need to find the conditional modes, then evaluate the conditional variances, then step out according to the conditional variance, evaluate the integrand relative to the step. The paper by Sophia Rabe-Hesketh et al describes a spherical form of the Gauss-Hermite quadrature formula. Look that up and use it. Because the Gauss-Hermite quadrature is formed as a sum, it is necessary to divide the contributions to the deviance according to the levels of the random effects. This means that it is only practical to use AGQ when the response vector can be split into sections that are conditionally independent. As far as I can see this will mean a single grouping factor only. ----- Allow for a matrix of responses in lmer so multiple fits can be performed without needing to regenerate the model matrices. ----- Modify the one-argument form of the anova method for lmer objects (yet again) to calculate the F ratios. It is the df, not the ratio that is controversial. Should there be an extractor function for the mean square error? If so, what should it be called? ----- Determine what a "coef" function should do for multiple, possibly non-nested, grouping factors. ----- Determine why the names of the components of the values of the ranef and coef extractor methods are not printed. ----- - add nicer (more realistic?) pedigree examples and tests - document print() including an example print(, corr = FALSE)