Package index • omopduckdb

Schema and vocabulary

Initialize CDM tables and manage standard Athena vocabularies.

build_schema(): Build the OMOP CDM v5.4 schema into a DuckDB database file
load_vocabulary(): Load or refresh the OHDSI Athena vocabulary into the OMOP CDM database
attach_central_vocabulary(): Attach a Central Vocabulary Database via DuckDB Zero-Copy Views
read_csv_sql() read_parquet_sql() attach_database() sql_copy_to(): SQL table-function expressions and file operations with sanitized paths
omop_connect(): Connect to an OMOP DuckDB Database with the Central Vocabulary Attached Transparently
omop_connect_lakehouse(): Delta Lake and Apache Iceberg Lakehouse Connector for OMOP DuckDB
load_from_bigquery(): Copy OMOP-shaped table(s) from BigQuery into the local DuckDB CDM database
check_vocabulary_version(): Query vocabulary version and summary metrics from DuckDB OMOP CDM

PCORnet ETL and synthesizers

Ingest PCORnet extracts and generate required OHDSI eras and observation periods.

etl_pcornet(): Run the PCORnet -> OMOP CDM v5.4 ETL (R implementation)
build_observation_period(): Synthesize Observation Period envelopes
build_drug_era(): Synthesize Drug Eras
build_condition_era(): Synthesize Condition Eras

Multi-site Consortium Federation

Federated CDM queries and multi-site metadata initialization.

federated_consortium(): Multi-Site Federated Consortium Connector
consortium_query(): Execute a Query Across the Federated Consortium Views
consortium_site_summaries(): Audit Record Counts Across Participating Consortium Sites
consortium_calculate_scores(): Generate Consortium-Wide and Site-Stratified Bedside Risk Scores
consortium_generate_table1(): Generate Federated Table 1 Baseline Demographics and Clinical Profiles
consortium_generate_table2(): Generate Federated Table 2 Completeness and Missingness Audit
consortium_close(): Close Federated Consortium DuckDB Connection
create_federated_consortium(): Create a Zero-Copy Federated Consortium Connection
check_cross_database_discrepancy(): Check Cross-Database Concept Frequency Discrepancy
with_cell_suppression(): Create Privacy-Preserving View with Small-Cell Masking
with_differential_privacy(): Create Privacy-Preserving View with Differential Privacy Laplace Noise
audit_cell_suppression() consortium_privacy_audit(): Privacy-Preserving Cell Suppression Auditor
consortium_multivariable_risk_factors(): Robust Multivariable Odds Ratios with L2 / Ridge Inversion
consortium_cross_site_validation(): Internal-External Cross-Validation (IECV) Matrix
consortium_cohort_diagnostics(): Generate Pure DuckDB OHDSI Cohort Diagnostics
export_to_circe_json(): Export CohortFlow to OHDSI Circe JSON
build_manuscript_scaffold(): Build End-to-End Consortium Manuscript Scaffold
federated_meta_analysis(): Multi-Site Federated Meta-Analysis Aggregator
load_care_site(): Ingest PCORnet facility.csv and/or Seed Primary Institutional Care Site
load_cdm_source(): Populate CDM_SOURCE metadata table
load_location(): Maps lds_address_history.csv into OMOP CDM location table and updates person.location_id

Vocabulary mapping and Usagi

Import custom crosswalks and audit unmapped source codes.

import_source_to_concept_map(): Ingest custom mappings from a CSV into the OMOP source_to_concept_map table
import_usagi_mappings(): Import mappings exported from OHDSI Usagi into source_to_concept_map
export_unmapped_codes(): Export unmapped (concept_id = 0) codes aggregated by frequency ready for OHDSI Usagi
auto_remap_unmapped(): Heuristic Remapping and Diagnostic Repair for Unmapped Records

CDM table remapping

Retroactively update existing CDM tables to new vocabulary concepts.

remap_cdm_table(): Remap unmapped (concept_id = 0) or deprecated concepts in an OMOP table
remap_all(): Remap all supported clinical tables and optionally rebuild eras

Cohorts and concept algebra

Parameterized cohort builders, set operations, and transitive hierarchy algebra.

define_study_cohort(): Define an Index-Stay Study Cohort
build_readmission_cohort(): Build 30-Day Readmission Cohort
build_end_of_life_cohort() build_mortality_cohort(): Build End of Life / Mortality Cohort
build_treatment_episodes(): Collapse Longitudinal Medication Exposures into Continuous Treatment Episodes
generate_consort_attrition(): Generate CONSORT Attrition Flowchart Object
to_mermaid(): Convert Object to Mermaid Diagram
to_typst(): Convert Object to Typst Flowchart
to_tikz(): Convert Object to LaTeX TikZ Flowchart
prepare_competing_risks_data(): Prepare Competing Risk Survival Data for Readmission vs. Mortality
create_cohort(): Materialize an OMOP Cohort
combine_cohorts(): Combine Cohorts via Set Operations
compute_attrition(): Track Patient Attrition Across Phenotyping Rules (CONSORT Flowchart)
get_cohort_summary(): Get Cohort Summary Statistics
ensure_cohort_tables(): Ensure Standard OMOP Cohort Tables Exist
resolve_concept_set(): Resolve Concept Set Algebra (ATLAS-Compatible)
get_concept_ancestors(): Resolve Concept Ancestors from Hierarchy Transitive Closure
get_concept_descendants(): Resolve Concept Descendants from Hierarchy Transitive Closure

Native CIRCE cohort compiler

In-process compilation and execution of OHDSI ATLAS / CIRCE JSON definitions in DuckDB.

circe: Native CIRCE / Atlas JSON-to-DuckDB Vectorized SQL Compiler
compile_circe_to_duckdb(): Compile CIRCE JSON to Vectorized DuckDB SQL
execute_circe_cohort(): Execute CIRCE JSON Cohort Directly in DuckDB

Comorbidity profiling and phenotyping

Validated 31 Elixhauser domains, Charlson comorbidity index, and composite risk scoring.

extract_elixhauser_comorbidities(): Elixhauser comorbidity profile (31 domains, van Walraven score)
extract_charlson_index(): Charlson comorbidity profile (17 categories, Charlson index)
calculate_bedside_scores(): Bedside Clinical Risk Score Calculators for OMOP CDM Cohorts
calculate_lace_index(): Calculate the LACE Index score for an acute hospital cohort
calculate_hospital_score(): Calculate the HOSPITAL Score for potentially avoidable 30-day readmissions
scores: Clinical Benchmark Risk Score Calculators (LACE Index and HOSPITAL Score)
list_available_phenotypes(): List available pre-compiled phenotype bundle names
get_phenotype_concept_set(): Retrieve pre-compiled concept set definition for an OHDSI phenotype
phenotypes: Pre-compiled OHDSI Phenotype Library Concept Bundles

Machine learning feature extraction

Extract patient-level sparse concept matrices, windowed temporal features, and measurements.

extract_sparse_concept_matrix(): Sparse concept matrix for ML / PLP-style models
extract_patient_features() extract_features(): Extract Machine Learning Feature Matrix from OMOP CDM
extract_temporal_features(): Extract Temporal Baseline and Prior Utilization Features
extract_clinical_sequence_tokens(): Extract Foundation Model Clinical Sequence Tokens
aggregate_concept_sets(): Aggregate Concept Sets via Athena Ancestor Traversal
extract_measurements(): Extract Consolidated Measurements and Vitals
extract_standard_labs(): Extract Harmonized Inpatient Lab Panels with Outlier Sanitization
extract_inpatient_labs(): Index-stay laboratory values per cohort row, separating acute labs from routine ones
extract_prior_utilization(): Prior healthcare utilization per cohort row, strictly before the index admission
consortium_calibration(): Cross-Site Model Calibration and Decision Curve Analysis (DCA)
decision_curve_analysis(): Clinical Decision Curve Analysis (DCA)
consortium_explainability(): Consortium Tree Model Explainability and TRIPOD Reporting
CORE_14_LAB_PANEL: Curated Core 14 Inpatient Lab Panel LOINCs
ACUTE_LAB_PANEL: Acute inpatient lab LOINCs (lactate, troponin I, arterial blood gases)
WORST_LAB_DIRECTION: Direction in which each lab is clinically "worse" (used by strategy = "worst")
DEFAULT_VISIT_CONCEPT_GROUPS: Default visit-concept groups for extract_prior_utilization()

Production scoring and train/serve skew guard

A fitted imputer, scaler and model bundled with the ordered feature schema; scoring refuses frames that do not match it.

omop_scoring_pipeline(): Production scoring bundle with a train/serve skew guard
fit_scoring_pipeline(): Fit a scoring pipeline on a training data frame
validate_scoring_input(): Validate a frame against a scoring pipeline's schema
predict_scoring_pipeline() predict_scoring_class(): Score new patients with a scoring pipeline
save_scoring_pipeline() load_scoring_pipeline(): Save / load a scoring pipeline bundle
scoring_pipeline_out_of_range(): Rows per feature outside the min/max seen at fit time (a drift check)

Ecosystem bridges (HADES & tidymodels)

Interoperability with OHDSI HADES PatientLevelPrediction and R tidymodels / orbital scoring.

hades_preflight(): Check that a DuckDB file is ready for OHDSI HADES (PatientLevelPrediction / DeepPatientLevelPrediction)
plp_database_details(): PatientLevelPrediction database details for an omop-duck-db DuckDB file
cm_database_details(): CohortMethod database connection details for an omop-duck-db DuckDB file
as_plp_data(): Turn a sparse concept matrix into a PatientLevelPrediction plpData
export_plp_study_package(): Automated OHDSI PatientLevelPrediction Study Package Generator
duckdb_to_andromeda(): Direct Andromeda Zero-Copy View Adapter
as_andromeda(): Convert DuckDB Tables to an Andromeda Object
as_tidymodels_data(): Sparse concept matrix as tidymodels-ready data
model_concepts(): Concepts a fitted model actually uses
materialize_concept_features(): Write concept features to a DuckDB table, for in-database scoring

Data quality, Table 1, and Export

Run Data Quality Dashboard checks, build publication-grade Table 1s, and export datasets.

run_dqd(): Run Native DuckDB DataQualityDashboard (DQD) Engine
sanitize_measurements(): Sanitize implausible numeric measurements (physiologic limits or statistical outliers)
generate_table1(): Generate Table 1 and Completeness Matrix
export_table1(): Export Table 1 or Table 1b to Markdown, LaTeX, Quarto, Typst, or CSV
table1_to_quarto(): Format Table 1 into Publication-Grade Quarto Manuscript Syntax
table1_to_typst(): Format Table 1 into Native Typst Manuscript Table Syntax
to_markdown(): Convert Object to Markdown Table
to_latex(): Convert Object to LaTeX Table
validate_table1_reconciliation(): Validate Table 1 Reconciliation against Published Baseline
export_cdm(): Enterprise Multi-Target Export Engine for OMOP DuckDB CDM
export_to_parquet(): Export a CDM Table to Parquet
DEFAULT_LAB_LOINCS: Table 1 Descriptive Statistics Generator and Validation Harness
DEFAULT_MEDICATION_CONCEPTS: Default Core Chronic Medication Classes (ATC / RxNorm Ancestors)
DEFAULT_VITAL_LOINCS: Default Vital Sign LOINC Mappings

Causal inference and survival analysis

In-engine propensity score weighting (IPTW/SMR) and Kaplan-Meier / Competing Risks estimators.

generate_propensity_weights(): In-Engine Propensity Score and Inverse Probability of Treatment Weighting (IPTW) Module
estimate_km_survival(): Kaplan-Meier Survival and Fine-Gray Competing Risks Cumulative Incidence (CIF) Estimator
estimate_cif(): Cause-Specific Cumulative Incidence Function (CIF) Estimator
fine_gray_estimator(): Fine-Gray Subdistribution Hazard Estimator
plot_cif(): Plot Cumulative Incidence Function (CIF)

Wide-table ingestion, datetime normalization and file paths

Staged one-concept-per-commit loading of wide measurement tables, vectorized timestamp parsing, safe SQL file paths, and ETL source-view helpers.

load_wide_measurement(): Staged, sequential ingestion of wide measurement tables
DEFAULT_VITAL_CONCEPT_MAPPING: Default PCORnet VITAL concept mapping for load_wide_measurement()
DEFAULT_VITAL_PLAUSIBILITY_FILTERS: Default clinical plausibility bounds for PCORnet vitals
vectorized_strptime() normalize_time_sql(): Vectorized, fault-tolerant datetime normalization for messy EHR feeds
install_vectorized_strptime(): Register normalize_time() and vectorized_strptime() SQL macros on a connection
DEFAULT_DATETIME_FORMATS: Default prioritized datetime formats
sanitize_sql_path() sql_path_literal(): Cross-platform path sanitization for DuckDB file operations
read_csv_sql() read_parquet_sql() attach_database() sql_copy_to(): SQL table-function expressions and file operations with sanitized paths
build_safe_source_view(): Fault-tolerant view over a source CSV

Resource governor

Size DuckDB memory, thread count and disk-spill location to the host.

configure_resources(): Size a DuckDB connection to the host: RAM limit, disk-spill location and threads
detect_system_ram(): Detect physical system RAM

Medication-class strategies and phenotype DSL

Dual-strategy (source crosswalk vs OMOP hierarchy) medication classification with a per-site kappa auditor, and a composable phenotype cohort DSL compiled to single-pass SQL.

extract_medication_classes(): Dual-strategy medication-class classifier
extract_medication_transitions(): Extract medication trajectory flags across care transitions
medication_transitions(): Transitions-of-care medication trajectories and trajectory summary statistics
audit_medication_strategies(): Per-site agreement audit of the source-crosswalk and OMOP-hierarchy strategies
condition_criteria() measurement_criteria() drug_criteria(): Composable phenotype cohort DSL compiled to single-pass DuckDB SQL
everyone(): All persons (base population for !x)
phenotype_union() phenotype_intersect() phenotype_not(): Logical operators over phenotype expressions
phenotype_to_list() phenotype_from_list(): Convert a phenotype expression to / from a plain list
compile_phenotype(): Compile a phenotype expression to DuckDB SQL
execute_phenotype(): Compile and run a phenotype into a cohort table

Bounds, units, domain profiles and cohort flow

Physiologic bounding and unit harmonization, clinical domain profiles for Table 1, medication windows, CONSORT cohort flow, completeness profiling and the Arrow hand-off to PatientLevelPrediction.

DEFAULT_PHYSIOLOGIC_BOUNDS: Default physiologic plausibility bounds (Adult)
POPULATION_PHYSIOLOGIC_BOUNDS: Pluggable physiologic bounding and unit harmonization for measurements
DEFAULT_UNIT_RULES: Default unit-conversion rules
resolve_bounds(): Resolve bounds: defaults merged with user overrides
filter_bounds(): Apply physiologic bounds to a data frame
harmonize_measurement_units(): Unit-normalization preflight for the measurement table
harmonize_anthropometrics(): Anthropometric Harmonization (Height, Weight, and Calculated BMI)
medication_extractor(): Medication exposure engine bound to a connection and drug-class specs
domain_profiles: Modular clinical domain profiles and table1_generator()
domain_profile_names(): Built-in domain profile names
load_domain_profile(): Load a domain profile
table1_generator(): Table 1 generator extended by clinical domain profiles
cohort_flow(): Sequential cohort filters, one set of counts, CONSORT outputs and pre-flight checks
to_graphviz(): Convert an attrition table to Graphviz DOT source
generate_table2(): Completeness / sentinel profiler ("Table 2")
as_plp_arrow(): Stream a wide feature table into PatientLevelPrediction-shaped Arrow tables
plp_arrow_to_plp_data(): Turn as_plp_arrow() output into a plpData object

Large-file sync

Synchronize DuckDB databases and vocabulary files via Google Drive.

gdrive_upload_snapshot(): Upload the large generated artifacts to Google Drive
gdrive_download_latest(): Download the latest snapshot from Google Drive

Advanced analytics, causal inference, and data quality (v0.6.4)

Negative controls, semantic concept set optimizer, trajectory features, federated survival, and synthetic twins.

add_missing_indicators(): Add Missing Indicator Columns
assert_exposure_coverage(): Assert Non-Zero Medication Exposure Coverage Across Sites
audit_code_list_coverage(): Audit Clinical Code List Coverage
calibrate_estimates(): Calibrate Nominal Study Estimates Using Empirical Null Distribution
check_vocabulary_provenance(): Check Vocabulary Provenance Between Databases
cohort_diagnostics: OHDSI Cohort Diagnostics in Pure DuckDB
cohort_flow_tools: cohort_flow() (computable phenotype + CONSORT), generate_table2() (completeness profiler) and as_plp_arrow() (in-memory Arrow hand-off to PatientLevelPrediction)
cohort_from_index_stays(): Force Study Cohort Index Stays from an External List
cohort_overlap(): Cohort Reconciliation and Membership Overlap
compare_feature_tables(): Feature Parity Ledger Comparing Two Derivations
concept_optimizer: Semantic Concept Set Optimizer & Usagi Auto-Suggester Module
consortium_patient_overlap(): Consortium Patient Overlap
consortium_propensity_score(): Consortium Causal Inference Propensity Engine
consortium_survival_meta_analysis(): Multi-Site Federated Survival Meta-Analysis
detect_redundant_features(): Detect Redundant and Derived Features (Constant Ratios, Differences, Collinearities)
dqd_summary(): Verify DQD Pass-Rate Mathematical Consistency and Reproducibility
explain_medication_disagreement(): Explain Strategy Disagreements for Medication Exposure
export_medication_crosswalk(): Export Medication Class Crosswalk from OMOP Hierarchy
extract_temporal_trajectories(): Extract Longitudinal Measurement Trajectory Features
federated_survival: Multi-Site Federated Survival Meta-Analysis Module
generate_negative_controls(): Generate Candidate Negative Control Concepts
generate_site_patient_tokens(): Generate Salted Site Patient Tokens
generate_synthetic_omop_twin(): Generate Synthetic OMOP Twin Database
integrity: Data Integrity, Statistical Leakage Scan, Informative Missingness, Provenance, and Overlap Tools
leakage_scan(): Statistical Leakage Scan
manuscript: End-to-End Manuscript Scaffold Builder
match_nearest_neighbor(): 1:k Nearest-Neighbor Caliper Matching on Propensity Scores
missingness_by_outcome(): Missingness Stratified by Outcome
negative_controls: Automated Negative Control & Empirical Calibration Module
numbers_of_record(): Numbers-of-Record Manifest
observation_period_diagnostics(): Observation Period Diagnostics
omopduckdb omopduckdb-package: omopduckdb: Build and Populate an OMOP CDM v5.4 Database in DuckDB
optimize_concept_set(): Optimize Concept Set and Suggest Missing Codes
panel_missingness_audit(): Audit Laboratory Panel Completeness and Discordant Missingness
planned_readmission_filter(): Planned-Readmission Filter
plausibility_report(): Table and Feature-Level Physiological Plausibility Report and CI Gate
plot_consort_flow(): Plot Visual Cohort Attrition Flow (CONSORT / Sankey)
plot_love_plot(): Generate SVG Love Plot for Covariate Balance
plot_multisite_attrition_heatmap(): Generate Multi-Site Attrition Heatmap
provenance(): Provenance Stamp
quality: Data Quality, Plausibility, and Feature Integrity Audits
reconciliation: Pipeline Reconciliation, Cohort Overlap, and Parity Ledger
run_study_grid(): Study Sensitivity Grid Execution
scan_document_numbers(): Scan Document Numbers for Stale Figures
synthetic: Automated Synthetic OMOP Twin Cohort Generator Module
trajectories: Longitudinal Temporal Trajectory Features Module
vocabulary_preflight(): Runs a comprehensive preflight verification of OMOP CDM vocabulary coverage, ATC hierarchies, unmapped concept 0 rates across clinical tables, and drug exposure types.

EHR prescriptions, adherence, and acute trajectories (v0.6.5 - v0.6.6)

Modern multi-vocabulary drug exposure routing, combination products, PDC adherence, unit discordance, and acute lab trajectories.

DEFAULT_PRESCRIBED_TYPE_CONCEPT_IDS DEFAULT_ADMINISTERED_TYPE_CONCEPT_IDS DEFAULT_DISPENSED_TYPE_CONCEPT_IDS: Default OMOP CDM concept sets for drug exposure types
KNOWN_UNIT_CONVERSIONS: Known laboratory unit conversions and standard ratios
audit_combination_products(): Combination Product Disaggregation Audit
compute_adherence_pdc(): Compute Adherence (PDC & MPR)
compute_drug_eras(): Compute Drug Eras On-The-Fly
detect_unit_discordance(): Detect Unit Discordance & Bimodal Normalization
export_companion_manifest(): Export Companion Identifier Manifest
extract_acute_trajectories(): Extract Acute In-Hospital Episode Trajectories
impute_encounter_observations(): Deterministic In-Encounter LOCF & Leakage-Safe Imputation
load_drug_exposure(): Load Drug Exposure Records from PCORnet Extract
load_macros(): Load SQL Macros into Connection
normalize_units(): Normalize Units
stream_to_parquet(): Stream object out-of-core to Parquet dataset
to_arrow_dataset(): Convert object to Arrow Dataset

Hierarchical rollups, tabular loaders, concept patterns, fuzzy mappings, domain provenance, and Parquet crosswalk cache (v0.6.7 - v0.6.8)

Declarative multi-domain hierarchical rollups, native tabular and workbook loaders, bundled clinical code lists, concept set pattern matching, DuckDB in-database fuzzy string matching, clinical provenance filters, Parquet crosswalk materialization, and vocabulary drift auditing.

audit_concept_drift(): Audit Concept Drift Across Vocabulary Releases
concept_pattern(): Define a Concept Pattern Specification
extract_rollup_exposures(): Extract Cohort Exposures Matched to Rollup
filter_conditions(): Filter Conditions by Diagnostic Provenance
filter_measurements(): Filter Measurements by Clinical Provenance
filter_visits(): Filter Visits by Clinical Provenance
hierarchical_rollup(): Define a Hierarchical Rollup Specification
hierarchical_rollup_from_bundled(): Load Hierarchical Rollup from Bundled Reference Code Lists
hierarchical_rollup_from_csv(): Load Hierarchical Rollup from a CSV File
hierarchical_rollup_from_excel(): Load Hierarchical Rollup from an Excel Worksheet
materialize_crosswalk(): Materialize Crosswalk from Rollup Specification
DEFAULT_INPATIENT_VISIT_TYPE_CONCEPT_IDS DEFAULT_OUTPATIENT_VISIT_TYPE_CONCEPT_IDS DEFAULT_EMERGENCY_VISIT_TYPE_CONCEPT_IDS DEFAULT_PRIMARY_CONDITION_TYPE_CONCEPT_IDS DEFAULT_ADMITTING_CONDITION_TYPE_CONCEPT_IDS DEFAULT_PROBLEM_LIST_CONDITION_TYPE_CONCEPT_IDS DEFAULT_LAB_RESULT_MEASUREMENT_TYPE_CONCEPT_IDS DEFAULT_ORDER_MEASUREMENT_TYPE_CONCEPT_IDS DEFAULT_POC_MEASUREMENT_TYPE_CONCEPT_IDS: Modern OMOP CDM Visit, Condition, and Measurement Provenance Concept IDs
resolve_concept_patterns(): Resolve Concept Patterns with Wildcards and Hierarchy Closures
rollup_engine(): Define a Rollup Engine Specification
rollup_engine_from_bundled(): Load Rollup Engine from Bundled Reference Code Lists
rollup_engine_from_csvs(): Load Rollup Engine from a Sequence of CSV Files
rollup_engine_from_excel(): Load Rollup Engine from an Excel Workbook
rollup_engine_from_folder(): Load Rollup Engine by Scanning a Directory of CSV Files
rollups: Extensible Hierarchical Rollups, Concept Pattern Matching, and Crosswalk Materialization
spec_hash(): Compute Cryptographic SHA-256 Hash of Rollup Specification
suggest_source_mappings(): In-Database Fuzzy and Partial Matching for Unmapped Codes