Package: OdysseusCharacterizationModule 0.0.1

Alexander Alexeyuk

OdysseusCharacterizationModule: Handy and Minimalistic Common Data Model Characterization

Extracts covariates from Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) domains using an R-only pipeline. Supports configurable temporal windows, domain-specific covariates for drug exposure, drug era (including Anatomical Therapeutic Chemical (ATC) groupings), condition occurrence, condition era, concept sets and cohorts. Methods are based on the Observational Health Data Sciences and Informatics (OHDSI) framework described in Hripcsak et al. (2015) <doi:10.1038/sdata.2015.35> and "The Book of OHDSI" OHDSI (2019, ISBN:978-1-7923-0589-8).

Authors:Alexander Alexeyuk [aut, cre]

OdysseusCharacterizationModule_0.0.1.tar.gz
OdysseusCharacterizationModule_0.0.1.zip(r-4.7-any)OdysseusCharacterizationModule_0.0.1.zip(r-4.6-any)
OdysseusCharacterizationModule_0.0.1.tar.gz(r-4.7-any)OdysseusCharacterizationModule_0.0.1.tar.gz(r-4.6-any)
OdysseusCharacterizationModule_0.0.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
OdysseusCharacterizationModule/json (API)

# Install 'OdysseusCharacterizationModule' in R:
install.packages('OdysseusCharacterizationModule', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • openjdk– OpenJDK Java runtime, using Hotspot JIT

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

openjdk

2.30 score 336 downloads 12 exports 43 dependencies

Last updated from:75e39ce275. Checks:6 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-develOK141
source / vignettesOK204
linux-releaseOK142
windows-develOK138
windows-releaseOK86
wasm-releaseOK123

Exports:buildConceptSetQueriesbuildConceptSetQuerycreateConceptSetTempTablecreateOcmCovariateSettingsdefineAnalysisWindowsexecuteSpecexecuteSpecsgetDbOcmCovariateDataplanAnalysisrenderAllSpecSqlrenderSpecSqlsingleNodeSetting

Dependencies:backportsbitbit64blobcheckmateclicliprcpp11crayonDatabaseConnectorDBIdbplyrdigestdplyrgenericsgluehmsjsonlitelifecyclemagrittrpillarpkgconfigprettyunitsprogresspurrrR6RcppreadrrJavarlangSqlRenderstringistringrtibbletidyrtidyselecttriebeardtzdburltoolsutf8vctrsvroomwithr

Custom CovariateData Builder — Eunomia Demo
Prerequisites | 1. Connect to Eunomia | 2. Create covariate settings | 3. Standalone usage — call the builder directly | Inspect the covariates table | Inspect the covariate reference | Inspect the analysis reference | 4. FeatureExtraction integration | 5. Combining with standard FeatureExtraction covariates | 6. Multiple domains and time windows | 7. Using concept-set features | Cleanup | Summary
Rendered fromcovariate-data-builder.Rmdusingknitr::rmarkdown

Last update: 2026-04-22
Started: 2026-04-22

OdysseusCharacterizationModule — Eunomia Walkthrough
Prerequisites | 1. Connect to Eunomia and create cohorts | 2. Define analysis windows | 3. Base feature — Condition Occurrence (start type) | 4. Base feature — Drug Exposure | 5. Base feature — Condition Era (overlap type) | 6. Base feature — Drug Era (overlap type) | 7. Base feature — Procedure Occurrence | 8. Base feature — Measurement | 9. Base feature — Observation | 10. Base feature — Visit Occurrence (overlap type) | 11. Non-aggregated (patient-level) output | 12. Multiple domains at once | 13. Cohort features — Using GiBleed cohort as a covariate | 14. Cohort features — Overlap type | 15. SQL rendering without execution | 16. Multiple time windows | 17. Combined — Base + Cohort features in one run | 18. Characterising a different cohort — Diclofenac | 19. Error handling with stopOnError = FALSE | 20. Cleanup | Session info
Rendered fromeunomia-walkthrough.Rmdusingknitr::rmarkdown

Last update: 2026-04-22
Started: 2026-04-22