R Econometrics MCP Server

# RMCP Streamlit Cloud Demo An interactive web interface demonstrating RMCP's econometric capabilities with Claude AI integration. ## 🌐 Live Demo **🚀 Try it now:** [RMCP on Streamlit Cloud](https://your-app-url.streamlit.app) ## 🔧 Local Development 1. **Install Dependencies:** ```bash pip install -r requirements.txt ``` 2. **Run the App:** ```bash streamlit run app.py ``` 3. **Open in Browser:** - Navigate to http://localhost:8501 ## ☁️ Deploy to Streamlit Community Cloud 1. **Fork this repository** 2. **Connect to Streamlit Cloud:** - Go to [share.streamlit.io](https://share.streamlit.io) - Connect your GitHub account - Select this repository - Set main file path: `streamlit/app.py` 3. **Deploy!** ## 🔧 Setup ### Claude API Key 1. Get your API key from [Claude Console](https://console.anthropic.com/) 2. Enter it in the sidebar when the app starts 3. The key is stored only for your session (not saved) ### Data Upload - Upload CSV files with your data - The app will show a preview and data information - All analyses work with your uploaded data ## 📊 Available Tools ### 📈 Descriptive Statistics - **Summary Statistics**: Mean, median, mode, variance, etc. - **Correlation Analysis**: Pearson and Spearman correlations - **Outlier Detection**: Identify and analyze outliers ### 📊 Regression Analysis - **Linear Regression**: Simple and multiple regression - **Logistic Regression**: Binary outcome modeling - **Panel Regression**: Fixed effects, random effects ### 🧪 Statistical Tests - **T-Tests**: One-sample, two-sample, paired - **Chi-Square Tests**: Independence testing - **Normality Tests**: Shapiro-Wilk, Kolmogorov-Smirnov - **Stationarity Tests**: Augmented Dickey-Fuller ### 📉 Time Series Analysis - **ARIMA Models**: Autoregressive integrated moving average - **VAR Models**: Vector autoregression - **Forecasting**: Time series predictions - **Cointegration Tests**: Long-run relationships ### 🔄 Data Transformations - **Lag Variables**: Create lagged versions - **Difference Variables**: First/second differences - **Winsorization**: Outlier treatment - **Standardization**: Z-score, min-max scaling ### 📊 Visualizations - **Scatter Plots**: With trend lines and grouping - **Time Series Plots**: Multiple variables over time - **Histograms**: Distribution analysis - **Correlation Heatmaps**: Visual correlation matrices ### 🎯 Advanced Econometrics - **Instrumental Variables**: 2SLS estimation - **Panel Fixed Effects**: Within-group estimation - **Difference-in-Differences**: Causal inference - **Regression Discontinuity**: Threshold effects ### 🤖 Machine Learning - **Random Forest**: Ensemble modeling - **Decision Trees**: Classification and regression - **Clustering**: K-means, hierarchical - **PCA Analysis**: Dimensionality reduction ## 🤖 Claude AI Assistant The built-in Claude AI assistant can help you: - Choose appropriate statistical methods - Interpret analysis results - Suggest next steps in your research - Explain complex statistical concepts Simply ask questions like: - "What regression method should I use for this data?" - "How do I interpret these coefficients?" - "What does this p-value mean?" ## 📁 Example Data The app works with any CSV file. Your data should have: - Column headers in the first row - Numeric variables for quantitative analysis - Categorical variables for grouping/factors - Time variables in standard formats (for time series) ## 🔒 Privacy & Security - All analysis runs locally with your R installation - Data is processed temporarily and not stored - Claude API key is session-only (not saved) - Uploaded files are automatically cleaned up ## 🛠️ Technical Details - **Backend**: R statistical computing via subprocess - **Frontend**: Streamlit web interface - **AI Integration**: Anthropic Claude API - **Data Processing**: Pandas DataFrames - **Visualization**: R ggplot2 + Plotly integration ## 📞 Support - Check the troubleshooting guide in `docs/troubleshooting.md` - Report issues on GitHub - Consult R documentation for statistical methods ## 🎯 Use Cases Perfect for: - **Academic Research**: Econometric analysis for papers - **Business Analytics**: Market research and forecasting - **Policy Analysis**: Causal inference and impact evaluation - **Financial Modeling**: Risk analysis and portfolio optimization - **Data Science**: Exploratory analysis and feature engineering --- *Powered by RMCP (R Model Context Protocol) - Making advanced econometrics accessible to everyone.*