analyze-stats

To generate reproducible statistics code for medical research, this Skill auto-detects the required analysis type from your dataset and outputs executable Python or R scripts. It drafts a formal plan and produces publication-quality tables, figures, and textual summaries.

Yes, you can create publication-quality tables and figures for biostatistics by loading your dataset. The process auto-detects endpoints, applies methodology templates, and outputs standardized formats like CSV, Markdown, and gtsummary-like tables ready for journal submission.

The best way to automate a formal statistical analysis plan is to provide your dataset directly. The system detects the data structure, drafts the plan outlining tests and assumptions, and generates reproducible code with validated methodology references.

Yes, this approach works for both Python and R statistical workflows. It generates reproducible code in either language, ensuring methodology compliance and high-quality visuals consistent with medical journal standards.

To ensure methodology compliance and validation when running medical statistics, the system loads on-demand templates and references. This guarantees that best practices for data assessment, validation, and visualization are followed throughout the end-to-end workflow.

Medical data analyses that can be auto-detected from the dataset structure include demographics, diagnostic accuracy, regression, and survival analysis. The system determines the appropriate tests and endpoints to produce manuscript-ready results.