r-data-validation

What problem does it solve?

Build auditable data-quality validation pipelines in R using pointblank, separating missingness, business logic, and extraction steps for traceability and reproducibility.

Core Features & Use Cases

• Modular validation plans: create one agent per source table to manage missingness checks, cross-field logic, and extraction of failed rows.
• Auditable workflow: separate missingness from logic, assign stable step_ids, and interrogate results for review and remediation.
• Use Case: For clinical data screening, validate datasets against predefined contracts and produce extracts for data cleaning workflows.

Quick Start

Define a validation contract for a dataset and implement one pointblank agent to validate missingness and cross-field rules.