# Research Methods Library

> Study design recommendations, identifying assumptions, runnable R and Python code, and citations from methodological texts on causal inference, econometrics, statistics, experimental design, marketing research, credit scoring, and machine learning. A Model Context Protocol (MCP) server; free, read-only, and requires no account or API key.

MCP endpoint (Streamable HTTP, no authentication): https://research-methods-mcp.vercel.app/mcp

## Connect

- Claude (web, desktop, and mobile): Settings > Connectors > Add custom connector, with the endpoint above.
- Claude Code: `claude mcp add --transport http research-methods https://research-methods-mcp.vercel.app/mcp`
- ChatGPT: enable developer mode, then add a custom connector of type MCP server with the endpoint above and no authentication.
- Cursor, Visual Studio Code, Windsurf, Cline, Zed, and other MCP clients: {"mcpServers": {"research-methods": {"url": "https://research-methods-mcp.vercel.app/mcp"}}}

## Tools

- choose_method: recommends study designs and methods for a research question, with assumptions, R and Python packages, and readings.
- search and fetch: find and read sections, with code, links, and citations.
- get_code: runnable R, Python, or Julia code for an analysis task.
- cite: references in APA, Chicago, MLA, Harvard, BibTeX, or RIS for the sections consulted.
- list_sources: the works in the library, with licenses and citations.

## Sources (17,777 sections)

- [A Guide on Data Analysis](https://mike-data-analysis.share.connect.posit.cloud/): Data analysis, econometrics, and causal inference in R: regression, GLMs, mixed models, experiments, difference-in-differences, synthetic control, regression discontinuity, instrumental variables, matching, double machine learning, mediation, sensitivity analysis, and structural IO.
- [causalverse: The All-in-One Causal Inference Toolkit](https://mikenguyen13.github.io/causalverse/): R package for the full causal inference pipeline: RCTs and power, DiD and staggered adoption, synthetic DiD, synthetic control, RD, IV, event studies, matching, heterogeneous effects, mediation, sensitivity, and publication-ready output. Vignettes plus a function reference.
- [Credit Scoring: Theory, Methods, and Practice](https://mikenguyen13.github.io/credit_score/): Executable handbook on credit scoring in Python: scorecards, PD models, survival and time-to-default, reject inference, gradient boosting, deep learning, explainability, fairness, regulation (Basel, IFRS 9, CECL), and production deployment.
- [Marketing Research: A Technical and Academic Treatment](https://mike-marketing-research.share.connect.posit.cloud/): Marketing research constructs, substantive domains (branding, advertising, pricing, CLV, influencer marketing, privacy), and methods (metrics, measurement, empirical and structural models) with reproducible R code.
- [AI in Action: A Reference for Artificial Intelligence](https://mikenguyen13.github.io/ai_in_action/): Implementation-first reference for modern AI in Python: foundations, machine learning, deep learning, large language models, retrieval-augmented generation, and AI agents.
- [Advanced Data Analysis](https://advanced-data-analysis.netlify.app/): Prediction-focused companion to A Guide on Data Analysis, in R: machine learning, tree ensembles, regularization, deep learning, reinforcement learning, model evaluation and interpretation, MLOps, and applications of large language models.
- [Applying Tidy Finance with Python to Vietnam](https://mikenguyen13.github.io/tidy_finance_vn/): Empirical finance and asset pricing with Python on Vietnamese market data: data access and cleaning, returns, CAPM and factor models, portfolio sorts, Fama-MacBeth regressions, and fixed income.
- [Tài Chính Ứng Dụng với Python](https://mikenguyen13.github.io/tidy_finance_vn_vi/): Vietnamese edition of Applying Tidy Finance with Python to Vietnam: empirical finance and asset pricing with Python on Vietnamese market data.
- [Machine Learning in Python](https://mikenguyen13.github.io/mlpy/): Machine learning theory and industry applications in Python: supervised learning, regularization, optimization, matrix factorization, credit scoring and approval, fraud detection, text similarity, approximate nearest neighbors, multi-armed bandits, and two-sided matching.
- [Research Articles and Publications](https://orcid.org/0000-0002-3432-8595): Peer-reviewed articles, Springer books, replication packages, and presentations by the author, from the ORCID record, with abstracts and DOIs.

## Optional

- [Installation and examples](https://research-methods-mcp.vercel.app/)
- [Source code and documentation](https://github.com/mikenguyen13/research-methods-mcp)
- [Privacy policy](https://research-methods-mcp.vercel.app/privacy)
- [Terms of use](https://research-methods-mcp.vercel.app/terms)