Minor pragmatic fixes and features

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://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).
• 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.