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brm_marginal_grid()
.sigma
in
brm_marginal_draws()
and
brm_marginal_summaries()
.outcome = "response"
with
reference_time = NULL
. Sometimes raw response is analyzed
but the data has no baseline time point.brm_data()
and encourage ordered
factors for the time variable (#113).brm_data_chronologize()
to ensure the correctness
of the time variable.brm_data()
. This helps
brm_data_chronologize()
operate correctly after calls to
brm_data()
.brms.mmrm_data
and
brms.mmrm_formula
to the brms
fitted model
object returned by brm_model()
.data
and formula
from the
above in brm_marginal_draws()
.effect_size
to
attr(formula, "brm_allow_effect_size")
.brm_data()
and
document examples.role
argument of brm_data()
in favor of reference_time
(#119).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).imputed
argument to accept a
mice
multiply imputed dataset (“mids”) in
brm_model()
(#121).summary()
method for
brm_transform_marginal()
objects.brm_transform_marginal()
.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.NA
s in
get_draws_sigma()
.summary()
messages for informative prior
archetypes.archetypes.Rmd
vignette using the FEV
dataset from the mmrm
package.brm_prior_template()
.formula
argument in
brm_marginal_draws()
."brm_data"
to
"brms_mmrm_data"
to align with other class names."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."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.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.brm_plot_compare()
.brm_transform_marginal()
to transform
model parameters to marginal means (#53).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).inference.Rmd
vignette.methods.Rmd
to model.Rmd
since
inference.Rmd
also discusses methods.brm_formula()
and
brm_marginal_draws()
to optionally model homogeneous
variances, as well as ARMA, AR, MA, and compound symmetry correlation
structures.brm_model()
to continuous families with
identity links.brm_prior_simple()
, deprecate the
correlation
argument in favor of individual
correlation-specific arguments such as unstructured
and
compound_symmetry
.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.brm_simulate_outline()
,
brm_simulate_continuous()
,
brm_simulate_categorical()
(#3).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.brm_data()
, deprecate level_control
in
favor of reference_group
.brm_data()
, deprecate level_baseline
in
favor of reference_time
.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.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.brm_marginal_draws()
to be more internally consistent and fit better with the addition of
subgroup-specific marginals (#18).brm_plot_compare()
and
brm_plot_draws()
to select the x axis variable and faceting
variables.brm_plot_compare()
to choose the primary
comparison of interest (source of the data, discrete time, treatment
group, or subgroup level).These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.