center argument to
brms_formula.default() and explain intercept parameter
interpretation concerns (#128).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://paulbuerkner.com/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.NAs 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).
Need a high-speed mirror for your open-source project?
Contact our mirror admin team at info@clientvps.com.
This archive is provided as a free public service to the community.
Proudly supported by infrastructure from VPSPulse , RxServers , BuyNumber , UnitVPS , OffshoreName and secure payment technology by ArionPay.