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