Package index
-
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 animputation_list_df()
object (i.e. a list ofimputation_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 multipleimputation_df()
's by ID
-
str_contains()
- Does a string contain a substring
-
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