| Add a class | add_class |
| Adjust trajectories due to the intercurrent event (ICE) | adjust_trajectories |
| Adjust trajectory of a subject's outcome due to the intercurrent event (ICE) | adjust_trajectories_single |
| Analyse Multiple Imputed Datasets | analyse |
| Analysis of Covariance | ancova |
| Implements an Analysis of Covariance (ANCOVA) | ancova_single |
| Antidepressant trial data | antidepressant_data |
| Applies delta adjustment | apply_delta |
| Construct an 'analysis' object | as_analysis |
| as_ascii_table | as_ascii_table |
| Set Class | as_class |
| as_cropped_char | as_cropped_char |
| Convert object to dataframe | as_dataframe |
| Creates a 'draws' object | as_draws |
| Create an imputation object | as_imputation |
| Convert indicator to index | as_indices |
| Creates a "MMRM" ready dataset | as_mmrm_df |
| Create MMRM formula | as_mmrm_formula |
| Expand 'data.frame' into a design matrix | as_model_df |
| Creates a simple formula object from a string | as_simple_formula |
| As array | as_stan_array |
| Create vector of Stratas | as_strata |
| Assert that all variables exist within a dataset | assert_variables_exist |
| Convert character variables to factor | char2fct |
| Diagnostics of the MCMC based on ESS | check_ESS |
| Diagnostics of the MCMC based on HMC-related measures. | check_hmc_diagn |
| Diagnostics of the MCMC | check_mcmc |
| Compute covariance matrix for some reference-based methods (JR, CIR) | compute_sigma |
| Convert list of 'imputation_list_single()' objects to an 'imputation_list_df()' object (i.e. a list of 'imputation_df()' objects's) | convert_to_imputation_list_df |
| Calculate delta from a lagged scale coefficient | d_lagscale |
| Create a delta 'data.frame' template | delta_template |
| Fit the base imputation model and get parameter estimates | draws draws.approxbayes draws.bayes draws.bmlmi draws.condmean |
| Evaluate a call to mmrm | eval_mmrm |
| Expand and fill in missing 'data.frame' rows | expand expand_locf fill_locf |
| Extract Variables from string vector | extract_covariates |
| Set to NA outcome values that would be MNAR if they were missing (i.e. which occur after an ICE handled using a reference-based imputation strategy) | extract_data_nmar_as_na |
| Extract draws from a 'stanfit' object | extract_draws |
| Extract imputed dataset | extract_imputed_df |
| Extract imputed datasets | extract_imputed_dfs |
| Extract parameters from a MMRM model | extract_params |
| Fit the base imputation model using a Bayesian approach | fit_mcmc |
| Fit a MMRM model | fit_mmrm |
| Generate data for a single group | generate_data_single |
| Creates a stack object populated with bootstrapped samples | get_bootstrap_stack |
| Derive conditional multivariate normal parameters | get_conditional_parameters |
| Get delta utility variables | get_delta_template |
| Fit the base imputation model on bootstrap samples | get_draws_mle |
| Extract the Effective Sample Size (ESS) from a 'stanfit' object | get_ESS |
| Von Hippel and Bartlett pooling of BMLMI method | get_ests_bmlmi |
| Simulate a realistic example dataset | get_example_data |
| Creates a stack object populated with jackknife samples | get_jackknife_stack |
| Fit MMRM and returns parameter estimates | get_mmrm_sample |
| Determine patients missingness group | get_pattern_groups |
| Get Pattern Summary | get_pattern_groups_unique |
| Expected Pool Components | get_pool_components |
| Derive visit distribution parameters | get_visit_distribution_parameters |
| Get imputation strategies | getStrategies |
| Does object have a class ? | has_class |
| if else | ife |
| Create a valid 'imputation_df' object | imputation_df |
| List of imputations_df | imputation_list_df |
| A collection of 'imputation_singles()' grouped by a single subjid ID | imputation_list_single |
| Create a valid 'imputation_single' object | imputation_single |
| Create imputed datasets | impute impute.condmean impute.random |
| Impute data for a single subject | impute_data_individual |
| Create imputed datasets | impute_internal |
| Sample outcome value | impute_outcome |
| invert | invert |
| Invert and derive indexes | invert_indexes |
| Is value absent | is_absent |
| Is character or factor | is_char_fact |
| Is single character | is_char_one |
| Is package in development mode? | is_in_rbmi_development |
| Is character, factor or numeric | is_num_char_fact |
| Last Observation Carried Forward | locf |
| R6 Class for Storing / Accessing & Sampling Longitudinal Data | longDataConstructor |
| Calculate design vector for the lsmeans | ls_design ls_design_counterfactual ls_design_equal ls_design_proportional |
| Least Square Means | lsmeans |
| Create a 'rbmi' ready cluster | make_rbmi_cluster |
| Set the multiple imputation methodology | method method_approxbayes method_bayes method_bmlmi method_condmean |
| Parallelise Lapply | par_lapply |
| Calculate parametric confidence intervals | parametric_ci |
| Pool analysis results obtained from the imputed datasets | as.data.frame.pool pool print.pool |
| Bootstrap Pooling via normal approximation | pool_bootstrap_normal |
| Bootstrap Pooling via Percentiles | pool_bootstrap_percentile |
| Internal Pool Methods | pool_internal pool_internal.bmlmi pool_internal.bootstrap pool_internal.jackknife pool_internal.rubin |
| Prepare input data to run the Stan model | prepare_stan_data |
| Print 'analysis' object | print.analysis |
| Print 'draws' object | print.draws |
| Print 'imputation' object | print.imputation |
| R6 Class for printing current sampling progress | progressLogger |
| P-value of percentile bootstrap | pval_percentile |
| QR decomposition | QR_decomp |
| Construct random effects formula | random_effects_expr |
| rbmi settings | rbmi-settings set_options |
| Capture all Output | record |
| recursive_reduce | recursive_reduce |
| Remove subjects from dataset if they have no observed values | remove_if_all_missing |
| Barnard and Rubin degrees of freedom adjustment | rubin_df |
| Combine estimates using Rubin's rules | rubin_rules |
| Sample Patient Ids | sample_ids |
| Create and validate a 'sample_list' object | sample_list |
| Sample random values from the multivariate normal distribution | sample_mvnorm |
| Create object of 'sample_single' class | sample_single |
| R6 Class for scaling (and un-scaling) design matrices | scalerConstructor |
| Set simulation parameters of a study group. | set_simul_pars |
| Set key variables | set_vars |
| Generate data | simulate_data |
| Simulate drop-out | simulate_dropout |
| Simulate intercurrent event | simulate_ice |
| Create simulated datasets | as_vcov simulate_test_data |
| Sort 'data.frame' | sort_by |
| Transform array into list of arrays | split_dim |
| Split a flat list of 'imputation_single()' into multiple 'imputation_df()''s by ID | split_imputations |
| R6 Class for a FIFO stack | Stack |
| Does a string contain a substring | str_contains |
| Strategies | strategies strategy_CIR strategy_CR strategy_JR strategy_LMCF strategy_MAR |
| string_pad | string_pad |
| Transpose imputations | transpose_imputations |
| Transpose results object | transpose_results |
| Transpose samples | transpose_samples |
| Generic validation method | validate |
| Validate analysis results | validate_analyse_pars |
| Validate a longdata object | validate_dataice validate_datalong validate_datalong_complete validate_datalong_notMissing validate_datalong_types validate_datalong_unifromStrata validate_datalong_varExists |
| Validate user specified strategies | validate_strategies |
| Validate 'analysis' objects | validate.analysis |
| Validate 'draws' object | validate.draws |
| Validate 'is_mar' for a given subject | validate.is_mar |
| Validate inputs for 'vars' | validate.ivars |
| Validate user supplied references | validate.references |
| Validate 'sample_list' object | validate.sample_list |
| Validate 'sample_single' object | validate.sample_single |
| Validate a 'simul_pars' object | validate.simul_pars |
| Validate a 'stan_data' object | validate.stan_data |
