Package: dejaVu 0.3.1

dejaVu: Multiple Imputation for Recurrent Events

Performs reference based multiple imputation of recurrent event data based on a negative binomial regression model, as described by Keene et al (2014) <doi:10.1002/pst.1624>.

Authors:Nikolas Burkoff [aut], Paul Metcalfe [aut], Jonathan Bartlett [aut, cre], David Ruau [aut]

dejaVu_0.3.1.tar.gz
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dejaVu.pdf |dejaVu.html✨
dejaVu/json (API)
NEWS

# Install 'dejaVu' in R:
install.packages('dejaVu', repos = c('https://jwb133.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/jwb133/dejavu/issues

Datasets:
  • simData - Simulated recurrent event data.

On CRAN:

Conda:

4.68 score 24 scripts 294 downloads 18 exports 1 dependencies

Last updated from:67b109fd30. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK113
source / vignettesOK248
linux-release-x86_64OK110
macos-release-arm64OK91
macos-oldrel-arm64OK85
windows-develOK82
windows-releaseOK77
windows-oldrelOK67
wasm-releaseOK93

Exports:ConstantRateDropcopy_referenceCreateNewDropoutMechanismCreateNewImputeMechanismCreateScenarioexpandEventCountextract_resultsGetImputedDataSetImportSimImputeLinearRateChangeDropMakeDejaDatanumberSubjectsSimfitSimulateCompleteSimulateDropoutsubjectsPerArmweighted_j2r

Dependencies:MASS

User Guide

Rendered fromUserGuide.Rnwusingknitr::knitron Apr 07 2026.

Last update: 2016-03-11
Started: 2015-12-08

Using dejaVu for imputation of an existing study dataset

Rendered fromanalysingExistingDatasets.Rnwusingknitr::knitron Apr 07 2026.

Last update: 2021-03-04
Started: 2017-03-03

Readme and manuals

Help Manual

Help pageTopics
Create a Dropout Mechanism with constant dropout rateConstantRateDrop
Create a copy reference 'ImputeMechanism' objectcopy_reference
A function which creates a DropOut Mechanism objectCreateNewDropoutMechanism
A function which creates an Impute Mechanism objectCreateNewImputeMechanism
Create 'Scenario' object from list of Fit SummariesCreateScenario
Data frame of covariates for simulating recurrent eventsDejaData.object
DropoutMechanism objectDropoutMechanism.object print.DropoutMechanism
Expand event counts into a list of event timesexpandEventCount
Extract the results of running a scenarioextract_results
Output a single imputed data setGetImputedDataSet
Import an existing data frame for use with the packageImportSim
Produce imputed data setsImpute
ImputeMechanism objectImputeMechanism.object
ImputeSim objectImputeSim.object
ImputeSimFit objectImputeSimFit.object
Create a Dropout Mechanism with drop out rate which changes by a fixed constant after every eventLinearRateChangeDrop
Create a 'DejaData' objectMakeDejaData
S3 generic to output the number of subjects in a given objectnumberSubjects
Scenario objectScenario.object
Simulated recurrent event data.simData
S3 generic for fitting modelsSimfit
Simulate a complete data setSimulateComplete
Simulate subject dropoutSimulateDropout
SingleSim Objectprint.SingleSim SingleSim.object summary.SingleSim
SingleSimFit objectSingleSimFit.object
S3 generic to output the number of subjects in each arm for a given objectsubjectsPerArm
summary.ImputeSimFit objectsummary.ImputeSimFit.object
summary.Scenario objectsummary.Scenario.object
summary.SingleSim objectsummary.SingleSim.object
summary.SingleSimFitsummary.SingleSimFit
Create a weighted_j2r 'ImputeMechanism' objectweighted_j2r