brms.mmrm 1.1.1

• Use FEV data in usage vignette.
• Show how to visualize prior vs posterior in the usage vignette.
• Add a center argument to brms_formula.default() and explain intercept parameter interpretation concerns (#128).

brms.mmrm 1.1.0

• 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://paulbuerkner.com/brms/articles/brms_missings.html (#121).
• Add a new imputed argument to accept a mice multiply imputed dataset (“mids”) in brm_model() (#121).
• Add a summary() method for brm_transform_marginal() objects.
• Do not recheck the rank of the formula in brm_transform_marginal().
• Support constrained longitudinal data analysis (cLDA) for informative prior archetypes brm_archetype_cells(), brm_archetype_effects(), brm_archetype_successive_cells(), and brm_archetype_successive_effects() (#125). We cannot support cLDA for brm_archetype_average_cells() or brm_archetype_average_effects() because then some parameters would no longer be averages of others.

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.