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All functions

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
eval_mmrm()
Evaluate a call to mmrm
expand() fill_locf() expand_locf()
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 parameters from a MMRM model
fit_mcmc()
Fit the base imputation model using a Bayesian approach
fit_mmrm()
Fit a MMRM model
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_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_in_rbmi_development()
Is package in development mode?
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_equal() ls_design_counterfactual() ls_design_proportional()
Calculate design vector for the lsmeans
lsmeans()
Least Square Means
make_rbmi_cluster()
Create a rbmi ready cluster
method_bayes() method_approxbayes() method_condmean() method_bmlmi()
Set the multiple imputation methodology
par_lapply()
Parallelise Lapply
parametric_ci()
Calculate parametric confidence intervals
pool() as.data.frame(<pool>) print(<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
set_options()
rbmi settings
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() as_vcov()
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
strategy_MAR() strategy_JR() strategy_CR() strategy_CIR() strategy_LMCF()
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_datalong_varExists() validate_datalong_types() validate_datalong_notMissing() validate_datalong_complete() validate_datalong_unifromStrata() validate_dataice()
Validate a longdata object
validate_strategies()
Validate user specified strategies