NEWS
brms.mmrm 1.1.1 (2024-10-02)
- 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 (2024-07-29)
- 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 (2024-06-25)
- 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()
.
brms.mmrm 1.0.0 (2024-06-04)
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 (2024-02-15)
- 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 (2023-08-18)
- Fix grammatical issues in the description.
brms.mmrm 0.0.1