- 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).

- 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.

- Handle outcome
`NA`

s 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()`

.

- 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.

- 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.

- 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.

- 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).

- 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).

- Fix grammatical issues in the description.

- First version.