Changelog • mmrm

mmrm 0.2.2

Add support for Kenward-Roger adjusted coefficients covariance matrix and degrees of freedom in mmrm function call with argument method. Options are “Kenward-Roger”, “Kenward-Roger-Linear” and “Satterthwaite” (which is still the default). Subsequent methods calls will respect this initial choice, e.g. vcov(fit) will return the adjusted coefficients covariance matrix if a Kenward-Roger method has been used.
Update the mmrm arguments to allow users more fine-grained control, e.g. mmrm(..., start = start, optimizer = c("BFGS", "nlminb")) to set the starting values for the variance estimates and to choose the available optimizers. These arguments will be passed to the new function mmrm_control.
Add new argument drop_visit_levels to allow users to keep all levels in visits, even when they are not observed in the data. Dropping unobserved levels was done silently previously, and now a message will be given. See ?mmrm_control for more details.

Previously duplicate time points could be present for a single subject, and this could lead to segmentation faults if more than the total number of unique time points were available for any subject. Now it is checked that there are no duplicate time points per subject, and this is explained also in the function documentation and the introduction vignette.
Previously in mmrm calls, the weights object in the environment where the formula is defined was replaced by the weights used internally. Now this behavior is removed and your variable weights e.g. in the global environment will no longer be replaced.

Deprecated free_cores() in favor of parallelly::availableCores(omit = 1).
Deprecated optimizer = "automatic" in favor of not specifying the optimizer. By default, all remaining optimizers will be tried if the first optimizer fails to reach convergence.

CRAN release: 2022-10-18

First CRAN version of the package.
The package fits mixed models for repeated measures (MMRM) based on the marginal linear model without random effects.
The motivation for this package is to have a fast, reliable (in terms of convergence behavior) and feature complete implementation of MMRM in R.

Currently 10 covariance structures are supported (unstructured; as well as homogeneous and heterogeneous versions of Toeplitz, auto-regressive order one, ante-dependence, compound symmetry; and spatial exponential).
Fast C++ implementation of Maximum Likelihood (ML) and Restricted Maximum Likelihood (REML) estimation.
Currently Satterthwaite adjusted degrees of freedom calculation is supported.
Interface to the emmeans package for computing estimated marginal means (also called least-square means) for the coefficients.
Multiple optimizers are run to reach convergence in as many cases as possible.
Flexible formula based model specification and support for standard S3 methods such as summary, logLik, etc.