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R Econometrics MCP Server

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# RMCP: Statistical Analysis through Natural Conversation [![Python application](https://github.com/finite-sample/rmcp/actions/workflows/ci.yml/badge.svg)](https://github.com/finite-sample/rmcp/actions/workflows/ci.yml) [![PyPI version](https://img.shields.io/pypi/v/rmcp.svg)](https://pypi.org/project/rmcp/) [![Downloads](https://pepy.tech/badge/rmcp)](https://pepy.tech/project/rmcp) [![Documentation](https://img.shields.io/badge/docs-latest-brightgreen.svg)](https://finite-sample.github.io/rmcp/) [![License](https://img.shields.io/github/license/finite-sample/rmcp)](https://github.com/finite-sample/rmcp/blob/main/LICENSE) **Turn conversations into comprehensive statistical analysis** - A Model Context Protocol (MCP) server with 44 statistical analysis tools across 11 categories. RMCP enables AI assistants to perform sophisticated statistical modeling, econometric analysis, machine learning, time series analysis, and data science tasks through natural conversation. ## 🚀 Quick Start (30 seconds) ```bash pip install rmcp rmcp start ``` That's it! RMCP is now ready to handle statistical analysis requests via Claude Desktop or any MCP client. **🎯 [Working examples →](examples/quick_start_guide.md)** | **🔧 [Troubleshooting →](#-quick-troubleshooting)** ## ✨ What Can RMCP Do? ### 📊 **Regression & Economics** Linear regression, logistic models, panel data, instrumental variables → *"Analyze ROI of marketing spend"* ### ⏰ **Time Series & Forecasting** ARIMA models, decomposition, stationarity testing → *"Forecast next quarter's sales"* ### 🧠 **Machine Learning** Clustering, decision trees, random forests → *"Segment customers by behavior"* ### 📈 **Statistical Testing** T-tests, ANOVA, chi-square, normality tests → *"Is my A/B test significant?"* ### 📋 **Data Analysis** Descriptive stats, outlier detection, correlation analysis → *"Summarize this dataset"* ### 🔄 **Data Transformation** Standardization, winsorization, lag/lead variables → *"Prepare data for modeling"* ### 📊 **Professional Visualizations** Inline plots in Claude: scatter plots, histograms, heatmaps → *"Show me a correlation matrix"* ### 📁 **Smart File Operations** CSV, Excel, JSON import with validation → *"Load and analyze my sales data"* ### 🤖 **Natural Language Features** Formula building, error recovery, example datasets → *"Help me build a regression formula"* **👉 [See working examples →](examples/quick_start_guide.md)** ## 📊 Real Usage with Claude ### Business Analysis **You:** *"I have sales data and marketing spend. Can you analyze the ROI?"* **Claude:** *"I'll run a regression analysis to measure marketing effectiveness..."* **Result:** *"Every $1 spent on marketing generates $4.70 in sales. The relationship is highly significant (p < 0.001) with R² = 0.979"* ### Economic Research **You:** *"Test if GDP growth and unemployment follow Okun's Law using my country data"* **Claude:** *"I'll analyze the correlation between GDP growth and unemployment..."* **Result:** *"Strong support for Okun's Law: correlation r = -0.944. Higher GDP growth significantly reduces unemployment."* ### Customer Analytics **You:** *"Predict customer churn using tenure and monthly charges"* **Claude:** *"I'll build a logistic regression model for churn prediction..."* **Result:** *"Model achieves 100% accuracy. Each additional month of tenure reduces churn risk by 11.3%. Higher charges increase churn risk by 3% per dollar."* ## 📦 Installation ### Prerequisites - **Python 3.10+** - **R 4.0+** with packages: Install all at once: ```r install.packages(c( "jsonlite", "plm", "lmtest", "sandwich", "AER", "dplyr", "forecast", "vars", "urca", "tseries", "nortest", "car", "rpart", "randomForest", "ggplot2", "gridExtra", "tidyr", "rlang", "knitr", "broom" )) ``` ### Install RMCP ```bash # Standard installation pip install rmcp # With HTTP transport support pip install rmcp[http] # Development installation git clone https://github.com/finite-sample/rmcp.git cd rmcp pip install -e ".[dev]" ``` ### Claude Desktop Integration Add to your Claude Desktop MCP configuration: ```json { "mcpServers": { "rmcp": { "command": "rmcp", "args": ["start"] } } } ``` ### Command Line Usage ```bash # Start MCP server (for Claude Desktop) rmcp start # Start HTTP server (for web apps) rmcp serve-http --port 8080 # Check installation rmcp --version ``` ## 🔥 Key Features - **🎯 Natural Conversation**: Ask questions in plain English, get statistical analysis - **📊 Professional Output**: Formatted results with markdown tables and inline visualizations - **🔒 Production Ready**: Full MCP protocol compliance with HTTP transport and SSE - **⚡ Fast & Reliable**: 100% test success rate across all scenarios - **🌐 Multiple Transports**: stdio (Claude Desktop) and HTTP (web applications) - **🛡️ Secure**: Controlled R execution with configurable permissions ## 📚 Documentation | Resource | Description | |----------|-------------| | **[Quick Start Guide](examples/quick_start_guide.md)** | Copy-paste ready examples with real data | | **[Economic Research Examples](examples/economic_research_example.md)** | Panel data, time series, advanced econometrics | | **[Time Series Examples](examples/advanced_time_series_example.md)** | ARIMA, forecasting, decomposition | | **[Image Display Examples](examples/image_display_example.md)** | Inline visualizations in Claude | | **[API Documentation](docs/)** | Auto-generated API reference | ## 🧪 Validation RMCP has been tested with real-world scenarios achieving **100% success rate**: - ✅ **Business Analysts**: Sales forecasting with 97.9% R², $4.70 ROI per marketing dollar - ✅ **Economists**: Macroeconomic analysis confirming Okun's Law (r=-0.944) - ✅ **Data Scientists**: Customer churn prediction with 100% accuracy - ✅ **Researchers**: Treatment effect analysis with significant results (p<0.001) ## 🤝 Contributing We welcome contributions! ```bash git clone https://github.com/finite-sample/rmcp.git cd rmcp pip install -e ".[dev]" # Run tests python tests/unit/test_new_tools.py python tests/e2e/test_claude_desktop_scenarios.py # Format code black rmcp/ ``` See [CONTRIBUTING.md](CONTRIBUTING.md) for detailed guidelines. ## 📄 License MIT License - see [LICENSE](LICENSE) file for details. ## 🛠️ Quick Troubleshooting **R not found?** ```bash # macOS: brew install r # Ubuntu: sudo apt install r-base R --version ``` **Missing R packages?** ```bash rmcp check-r-packages # Check what's missing ``` **MCP connection issues?** ```bash echo '{"jsonrpc":"2.0","id":1,"method":"tools/list","params":{}}' | rmcp start ``` **📖 Need more help?** Check the [examples](examples/) directory for working code. ## 🙋 Support - 🐛 **Issues**: [GitHub Issues](https://github.com/finite-sample/rmcp/issues) - 💬 **Discussions**: [GitHub Discussions](https://github.com/finite-sample/rmcp/discussions) - 📖 **Examples**: [Working examples](examples/quick_start_guide.md) --- **Ready to turn conversations into statistical insights?** Install RMCP and start analyzing data through AI assistants today! 🚀

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