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