v0.3.11

Bug Fixes

• Previously if a secondary optimizer fails mmrm will fail. This is fixed now.
• Previously character covariate variables will make Anova fail. This is fixed now.

What's Changed

• fix issue of h_record_all_outputs by @clarkliming in #424
• fix anova error by @clarkliming in #427
• remove biocVer by @clarkliming in #429
• update docs by @clarkliming in #428
• Missing data simulations by @danielinteractive in #430
• fix eigen deprecated warning by @clarkliming in #432
• add tests for residual.R, bump up version by @danielinteractive in #433

Full Changelog: v0.3.10...v0.3.11

v0.3.10

Miscellaneous

• Fix internal test skipping functions for MacOS R.

What's Changed

• 419: make skipping code more general to also run on old R versions by @danielinteractive in #420

Full Changelog: v0.3.8...v0.3.10

v0.3.7

Miscellaneous

• In documentation of mmrm_control(), the allowed vcov definition is corrected to "Empirical-Jackknife" (CR3), and "Empirical-Bias-Reduced" (CR2).
• Fix a compiler warning related to missing format specification in error message function call.

Full Changelog: v0.3.6...v0.3.7

v0.3.6

New Features

• The argument method of mmrm() now only specifies the method used for the
  degrees of freedom adjustment.
• Add empirical, empirical Jackknife and empirical bias-reduced adjusted coefficients
  covariance estimates, which can be specified via the new vcov argument of mmrm().
• Add residual and between-within degrees of freedom methods.
• Add Kenward-Roger support for spatial covariance structures.
• Add model.matrix() and terms() methods to assist in post-processing.
• Add predict() method to obtain conditional mean estimates and prediction intervals.
• Add simulate() method to simulate observations from the predictive distribution.
• Add residuals() method to obtain raw, Pearson or normalized residuals.
• Add tidy(), glance() and augment() methods to tidy the fit results into summary tables.
• Add tidymodels framework support via a parsnip interface.
• Add argument covariance to mmrm() to allow for easier programmatic access
  to specifying the model's covariance structure and to expose covariance
  customization through the tidymodels interface.

Bug Fixes

• Previously mmrm() follows the global option na.action and if it is set
  other than "na.omit" an assertion would fail. This is now fixed and hence NA
  values are always removed prior to model fitting, independent of the global
  na.action option.
• Previously a model.frame() call on an mmrm object with transformed terms, or new
  data, e.g. model.frame(mmrm(Y ~ log(X) + ar1(VISIT|ID), data = new_data),
  would fail. This is now fixed.
• Previously mmrm() always required a data argument. Now fitting mmrm can also use
  environment variables instead of requiring data argument. (Note that
  fit_mmrm is not affected.)
• Previously emmeans() failed when using transformed terms or not including the visit
  variable in the model formula. This is now fixed.
• Previously mmrm() might provide non-finite values in the Jacobian calculations,
  leading to errors in the Satterthwaite degrees of freedom calculations. This will raise
  an error now and thus alert the user that the model fit was not successful.

Miscellaneous

• Use automatic differentiation to calculate Satterthwaite adjusted degrees of freedom,
  resulting in 10-fold speed-up of the Satterthwaite calculations after the initial model fit.
• Add an interactive confirmation step if the number of visit levels is too large
  for non-spatial covariance structures. Use options(mmrm.max_visits = ) to specify the
  maximum number of visits allowed in non-interactive mode.
• Removed free_cores() in favor of parallelly::availableCores(omit = 1).
• The model.frame() method has been updated: The full argument is deprecated and
  the include argument can be used instead; by default all relevant variables are
  returned. Furthermore, it returns a data.frame the size of the number of observations
  utilized in the model for all combinations of the include argument
  when na.action= "na.omit".
• Overall, seven vignettes have been added to the package. All vignettes have a slightly
  different look now to reduce the size of the overall R package on CRAN.
• The used optimizer is now available via component(., "optimizer") instead of previously
  attr(., "optimizer").

Full Changelog: v0.2.2...v0.3.6

v0.2.2

New Features

• 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.

Bug Fixes

• 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.

Miscellaneous

• 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.

Full Changelog: v0.1.5...v0.2.2

v0.1.5

• First version of the package for fitting mixed models for repeated measures
  (MMRM) based on Template Model Builder (TMB).
• The motivation for this package is to have a fast, reliable (in terms of
  convergence behavior) and feature complete implementation of MMRM in R.

New Features

• Currently 9 covariance structures are supported (unstructured, as well as
  homogeneous and heterogeneous versions of Toeplitz, auto-regressive order one,
  ante-dependence, compound symmetry).
• Fast C++ implementation of Maximum Likelihood (ML) and Restricted Maximum
  Likelihood (REML) estimation.
• Satterthwaite adjusted degrees of freedom calculation.
• Interface to the emmeans package for computing estimated marginal means
  (also called least-square means) for the coefficients.
• Multiple optimizers are run to allow for as much convergence chance as possible.
• Flexible formula based model specification and support for standard S3 methods such
  as summary, logLik, etc.