Package: Characterization 4.1.0

Jenna Reps

Characterization: Implement Descriptive Studies Using the Common Data Model

An end-to-end framework that enables users to implement various descriptive studies for a given set of target and outcome cohorts for data mapped to the Observational Medical Outcomes Partnership Common Data Model.

Authors:Jenna Reps [aut, cre], Patrick Ryan [aut], Chris Knoll [aut]

Characterization_4.1.0.tar.gz
Characterization_4.1.0.zip(r-4.7-any)Characterization_4.1.0.zip(r-4.6-any)Characterization_4.1.0.zip(r-4.5-any)
Characterization_4.1.0.tgz(r-4.6-any)Characterization_4.1.0.tgz(r-4.5-any)
Characterization_4.1.0.tar.gz(r-4.7-any)Characterization_4.1.0.tar.gz(r-4.6-any)
Characterization_4.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
Characterization/json (API)

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

Bug tracker:https://github.com/ohdsi/characterization/issues

Pkgdown/docs site:https://ohdsi.github.io

Uses libs:
  • openjdk– OpenJDK Java runtime, using Hotspot JIT

On CRAN:

Conda:

openjdk

6.44 score 3 stars 33 scripts 731 downloads 20 exports 61 dependencies

Last updated from:0b7e342535. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK250
source / vignettesOK305
linux-releaseOK267
macos-releaseOK177
macos-oldrelOK206
windows-develOK247
windows-releaseOK228
windows-oldrelOK267
wasm-releaseOK171

Exports:cleanIncrementalcleanNonIncrementalcreateCaseSeriesSettingscreateCharacterizationSettingscreateCharacterizationTablescreateCohortIncidenceSettingscreateDechallengeRechallengeSettingscreateDuringCovariateSettingscreateRiskFactorSettingscreateSqliteDatabasecreateStudyPopulationSettingscreateTargetBaselineSettingscreateTimeToEventSettingsexampleOmopConnectionDetailsgetDbDuringCovariateDatainsertResultsToDatabaseloadCharacterizationSettingsrunCharacterizationAnalysessaveCharacterizationSettingsviewCharacterization

Dependencies:Andromedabackportsbitbit64blobcachemcheckmateclicliprcpp11crayonDatabaseConnectorDBIdbplyrdigestdplyrduckdbfastmapFeatureExtractiongenericsgluehmsjsonlitelaterlifecyclelubridatemagrittrmemoisememuseParallelLoggerpillarpkgconfigpoolprettyunitsprogresspurrrR6RcppreadrResultModelManagerrJavarlangRSQLiterstudioapisnowSqlRenderstringistringrtibbletidyrtidyselecttimechangetriebeardtzdburltoolsutf8vctrsvroomwithrxml2zip

Using Characterization Package
Introduction | Setup | Examples | Target Baseline Covariates | Risk Factor Covariates | Case Series Covariates | Dechallenge Rechallenge | Time to Event | Cohort Incidence | Run Multiple
Rendered fromUsingPackage.Rmdusingknitr::knitr

Last update: 2026-09-16
Started: 2024-08-09

Characterization Package Specification
Time-to-event | Inputs | Output | Worked Example | Example Inputs | Example Data Image | Example Data Table | Dechallenge-rechallenge | Example Data Plot | Intermediary Table | Intermediary Plots | Summary | Target Baseline Covariates | Outputs | Example Data | Results | Risk Factor Analysis | Intermedeiary Tables | Case Series
Rendered fromSpecification.Rmdusingknitr::knitr

Last update: 2026-07-31
Started: 2024-12-06

Readme and manuals

Help Manual

Help pageTopics
Removes csv files from folders that have not been marked as completed and removes the record of the execution filecleanIncremental
Removes csv files from the execution folder as there should be no csv files when running in non-incremental modelcleanNonIncremental
Create aggregate covariate study settingscreateCaseSeriesSettings
Create the settings for a large scale characterization studycreateCharacterizationSettings
Create the results tables to store characterization results into a databasecreateCharacterizationTables
Create cohort incidence study settingscreateCohortIncidenceSettings
Create dechallenge rechallenge study settingscreateDechallengeRechallengeSettings
Create during covariate settingscreateDuringCovariateSettings
Create risk factor study settingscreateRiskFactorSettings
Create an sqlite database connectioncreateSqliteDatabase
create the study population settingscreateStudyPopulationSettings
Create target baseline aggregate covariate study settingscreateTargetBaselineSettings
Create time to event study settingscreateTimeToEventSettings
create a connection detail for an example GI Bleed dataset from EunomiaexampleOmopConnectionDetails
Extracts covariates that occur during a cohortgetDbDuringCovariateData
Upload the results into a result databaseinsertResultsToDatabase
Load the characterization settings previously saved as a json fileloadCharacterizationSettings
execute a large-scale characterization studyrunCharacterizationAnalyses
Save the characterization settings as a jsonsaveCharacterizationSettings
viewCharacterization - Interactively view the characterization resultsviewCharacterization