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brms.mmrm 1.0.1.9008 (development)

  • Add brm_marginal_grid().
  • Show posterior samples of sigma in brm_marginal_draws() and brm_marginal_summaries().
  • Allow outcome = "response" with reference_time = NULL. Sometimes raw response is analyzed but the data has no baseline time point.
  • Preserve factors in brm_data() and encourage ordered factors for the time variable (#113).
  • Add brm_data_chronologize() to ensure the correctness of the time variable.
  • Do not drop columns in brm_data(). This helps brm_data_chronologize() operate correctly after calls to brm_data().
  • Add new elements brms.mmrm_data and brms.mmrm_formula to the brms fitted model object returned by brm_model().
  • Take defaults data and formula from the above in brm_marginal_draws().
  • Set the default value of effect_size to attr(formula, "brm_allow_effect_size").
  • Remove defaults from some arguments to brm_data() and document examples.
  • Deprecate the role argument of brm_data() in favor of reference_time (#119).
  • Add a new model_missing_outcomes in brm_formula() to optionally impute missing values during model fitting as described at https://paul-buerkner.github.io/brms/articles/brms_missings.html (#121).
  • Add a new imputed argument to accept a mice multiply imputed dataset ("mids") in brm_model() (#121).

brms.mmrm 1.0.1

  • Handle outcome NAs in get_draws_sigma().
  • Improve summary() messages for informative prior archetypes.
  • Rewrite the archetypes.Rmd vignette using the FEV dataset from the mmrm package.
  • Add brm_prior_template().

brms.mmrm 1.0.0

New features

  • Add informative prior archetypes (#96, #101).
  • Add [brm_formula_sigma()] to allow more flexibility for modeling standard deviations as distributional parameters (#102). Due to the complexities of computing marginal means of standard deviations in rare scenarios, [brm_marginal_draws()] does not return effect size if [brm_formula_sigma()] uses baseline or covariates.

Guardrails to ensure the appropriateness of marginal mean estimation

  • Require a new formula argument in brm_marginal_draws().
  • Change class name "brm_data" to "brms_mmrm_data" to align with other class names.
  • Create a special "brms_mmrm_formula" class to wrap around the model formula. The class ensures that formulas passed to the model were created by brms_formula(), and the attributes store the user's choice of fixed effects.
  • Create a special "brms_mmrm_model" class for fitted model objects. The class ensures that fitted models were created by brms_model(), and the attributes store the "brms_mmrm_formula" object in a way that brms itself cannot modify.
  • Deprecate use_subgroup in brm_marginal_draws(). The subgroup is now always part of the reference grid when declared in brm_data(). To marginalize over subgroup, declare it in covariates instead.
  • Prevent overplotting multiple subgroups in brm_plot_compare().
  • Update the subgroup vignette to reflect all the changes above.

Custom estimation of marginal means

  • Implement a new brm_transform_marginal() to transform model parameters to marginal means (#53).
  • Use brm_transform_marginal() instead of emmeans in brm_marginal_draws() to derive posterior draws of marginal means based on posterior draws of model parameters (#53).
  • Explain the custom marginal mean calculation in a new inference.Rmd vignette.
  • Rename methods.Rmd to model.Rmd since inference.Rmd also discusses methods.

Other improvements

  • Extend brm_formula() and brm_marginal_draws() to optionally model homogeneous variances, as well as ARMA, AR, MA, and compound symmetry correlation structures.
  • Restrict brm_model() to continuous families with identity links.
  • In brm_prior_simple(), deprecate the correlation argument in favor of individual correlation-specific arguments such as unstructured and compound_symmetry.
  • Ensure model matrices are full rank (#99).

brms.mmrm 0.1.0

  • Deprecate brm_simulate() in favor of brm_simulate_simple() (#3). The latter has a more specific name to disambiguate it from other simulation functions, and its parameterization conforms to the one in the methods vignette.
  • Add new functions for nuanced simulations: brm_simulate_outline(), brm_simulate_continuous(), brm_simulate_categorical() (#3).
  • In brm_model(), remove rows with missing responses. These rows are automatically removed by brms anyway, and by handling by handling this in brms.mmrm, we avoid a warning.
  • Add subgroup analysis functionality and validate the subgroup model with simulation-based calibration (#18).
  • Zero-pad numeric indexes in simulated data so the levels sort as expected.
  • In brm_data(), deprecate level_control in favor of reference_group.
  • In brm_data(), deprecate level_baseline in favor of reference_time.
  • In brm_formula(), deprecate arguments effect_baseline, effect_group, effect_time, interaction_baseline, and interaction_group in favor of baseline, group, time, baseline_time, and group_time, respectively.
  • Propagate values in the missing column in brm_data_change() such that a value in the change from baseline is labeled missing if either the baseline response is missing or the post-baseline response is missing.
  • Change the names in the output of brm_marginal_draws() to be more internally consistent and fit better with the addition of subgroup-specific marginals (#18).
  • Allow brm_plot_compare() and brm_plot_draws() to select the x axis variable and faceting variables.
  • Allow brm_plot_compare() to choose the primary comparison of interest (source of the data, discrete time, treatment group, or subgroup level).

brms.mmrm 0.0.2

  • Fix grammatical issues in the description.

brms.mmrm 0.0.1

  • First version.