Bayesian MMRMs using 'brms'

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Documentation for package ‘brms.mmrm’ version 1.1.0

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brms.mmrm-package brms.mmrm: Bayesian MMRMs using 'brms'
brm_archetype_average_cells Cell-means-like time-averaged archetype
brm_archetype_average_effects Treatment effect time-averaged archetype
brm_archetype_cells Cell means archetype
brm_archetype_effects Treatment effect archetype
brm_archetype_successive_cells Cell-means-like successive differences archetype
brm_archetype_successive_effects Treatment-effect-like successive differences archetype
brm_data Create and preprocess an MMRM dataset.
brm_data_change Convert to change from baseline.
brm_data_chronologize Chronologize a dataset
brm_formula Model formula
brm_formula.brms_mmrm_archetype Model formula
brm_formula.default Model formula
brm_formula_sigma Formula for standard deviation parameters
brm_marginal_data Marginal summaries of the data.
brm_marginal_draws MCMC draws from the marginal posterior of an MMRM
brm_marginal_draws_average Average marginal MCMC draws across time points.
brm_marginal_grid Marginal names grid.
brm_marginal_probabilities Marginal probabilities on the treatment effect for an MMRM.
brm_marginal_summaries Summary statistics of the marginal posterior of an MMRM.
brm_model Fit an MMRM.
brm_plot_compare Visually compare the marginals of multiple models and/or datasets.
brm_plot_draws Visualize posterior draws of marginals.
brm_prior_archetype Informative priors for fixed effects in archetypes
brm_prior_label Label a prior with levels in the data.
brm_prior_simple Simple prior for a 'brms' MMRM
brm_prior_template Label template for informative prior archetypes
brm_recenter_nuisance Recenter nuisance variables
brm_simulate_categorical Append simulated categorical covariates
brm_simulate_continuous Append simulated continuous covariates
brm_simulate_outline Start a simulated dataset
brm_simulate_prior Prior predictive draws.
brm_simulate_simple Simple MMRM simulation.
brm_transform_marginal Marginal mean transformation