picard is an R package for building and running reproducible, pipeline-based RWE studies using OHDSI tools. It provides a standardized study repository structure, manifest-driven input management, and execution workflows from development through production.

picard helps analysts manage the full lifecycle of an RWE pipeline, from study bootstrap through execution and dissemination.

picard initializes a consistent repository structure and project scaffolding via the Ulysses setup flow.

• Defines study metadata and contributors
• Configures one or more database blocks
• Creates standard folders, config files, and starter scripts
• Supports Git and reproducibility setup during initialization

picard centralizes DBMS connection and execution configuration through ExecutionSettings.

• Builds execution settings directly or from config blocks
• Separates schema settings from credential storage
• Supports environment-specific runs and cohort table naming conventions

picard uses manifest objects to register and track cohort and concept set inputs as durable study assets.

• Imports from ATLAS or adds programmatic definitions
• Registers custom SQL and derived cohorts
• Tracks metadata, hashes, dependencies, and status
• Supports mid-cycle maintenance (check, update, delete, reset)

picard provides file-generation helpers to reduce manual setup and enforce project conventions.

• Creates task scripts and reusable source utility files
• Creates SQL templates for analysis logic
• Creates pre-pipeline builder scripts for cohorts and concept sets
• Creates dissemination scripts for downstream reporting workflows

picard supports consistent pipeline execution across study tasks and database targets.

• Runs cohort generation and task pipelines with execution settings
• Tracks task execution state, rerun logic, and summaries
• Supports structured workflow from development to production runs

picard includes tools for combining and formatting outputs into analyst-ready deliverables.

• Binds results across database runs
• Standardizes outputs for comparison and downstream consumption
• Supports dissemination pipelines and print-friendly output generation

# install.packages("remotes")
remotes::install_github("OHDSI/Picard")
library(picard)

library(picard)

sm <- makeStudyMeta(
  studyTitle = "OMOP Characterization Study",
  therapeuticArea = "Endocrinology",
  studyType = "Characterization",
  contributors = list(
    setContributor(
      name = "Jane Doe",
      email = "jane.doe@institution.org",
      role = "developer"
    )
  )
)

db <- makeBlock(
  configBlockName = "my_cdm",
  cdmDatabaseSchema = "omop_cdm_schema",
  cohortTable = "study_cohorts",
  databaseName = "my_database_v1",
  databaseLabel = "Primary CDM"
)

ulySt <- makeUlyssesStudySettings(
  repoName = "my_study_project",
  repoFolder = "~/studies",
  studyMeta = sm,
  dbConnectionBlocks = list(db)
)

ulySt$initUlyssesRepo(verbose = TRUE, openProject = FALSE)

For detailed usage examples and end-to-end workflows, see the package site and vignettes.

1. Package site and articles: Articles
2. Full function reference: Reference
3. Changelog: NEWS

picard is under active development. APIs may evolve as pipeline and manifest workflows mature. Review NEWS before upgrading long-running studies.

Contributions, issues, and suggestions are welcome. Open an issue or pull request in the repository.