R Econometrics
Provides econometric modeling capabilities through R, enabling AI assistants to perform sophisticated analyses including linear regression, panel data models, instrumental variables regression, and diagnostic tests.
What is R Econometrics?
R Econometrics is a Model Context Protocol (MCP) server that allows AI assistants like Claude, Cursor, and VS Code to provides econometric modeling capabilities through r, enabling ai assistants to perform sophisticated analyses including linear regression, panel data models, instrumental variables regression, and di...
Provides econometric modeling capabilities through R, enabling AI assistants to perform sophisticated analyses including linear regression, panel data models, instrumental variables regression, and diagnostic tests.
This server falls under the Data Science & ML category on MCPgee, the world's largest MCP server directory with 33,000+ servers.
Features
- Provides econometric modeling capabilities through R, enabli
Use Cases
Maintainer
Works with
Installation
Manual Installation
npx r-econometrics-mcp-serverConfiguration
Configuration Details
claude_desktop_config.json
Performance
Response Metrics
Resource Usage
How to Set Up and Use R Econometrics
The R Econometrics MCP Server (rmcp) bridges AI assistants to a full R statistical environment, exposing 52 tools across 11 categories so you can run sophisticated econometric and statistical analyses directly from a conversation. It supports linear and panel data regression, instrumental variables, time series forecasting with ARIMA, clustering, random forests, and diagnostic tests — all backed by a curated set of 429 CRAN packages. Results including inline visualizations are returned to the AI assistant, making it possible to do publication-quality economic analysis without leaving your chat interface.
Prerequisites
- Python 3.10 or higher installed
- R 4.4.0 or higher installed and available on PATH
- Core R packages installed: jsonlite, dplyr, ggplot2, broom, plm, forecast, randomForest, AER, vars, mgcv
- pip package manager for installing rmcp
- An MCP-compatible client (Claude Desktop, Claude Code, or similar)
Install required R packages
Before installing rmcp, ensure the core R packages are available. Open an R session and install the key dependencies.
install.packages(c("jsonlite", "dplyr", "ggplot2", "broom", "plm",
"forecast", "randomForest", "rpart", "caret", "AER", "vars", "mgcv"))Install rmcp via pip
Install the rmcp Python package which acts as the MCP server layer that bridges your AI client to the R runtime.
pip install rmcpVerify the server starts correctly
Start the server manually to confirm it can locate your R installation and that all dependencies are in order.
rmcp startAdd the server to your MCP client configuration
Open your Claude Desktop config file and add the rmcp entry. The server will locate R automatically from your PATH.
{
"mcpServers": {
"r-econometrics": {
"command": "rmcp",
"args": ["start"],
"env": {
"RMCP_LOG_LEVEL": "INFO"
}
}
}
}Restart your client and run a test analysis
Restart Claude Desktop. The 52 econometric tools should now be available. Try a simple descriptive statistics request to confirm the R connection is working before running complex models.
R Econometrics Examples
Client configuration
Claude Desktop configuration block for the R Econometrics MCP server.
{
"mcpServers": {
"r-econometrics": {
"command": "rmcp",
"args": ["start"],
"env": {
"RMCP_LOG_LEVEL": "INFO",
"RMCP_R_TIMEOUT": "120"
}
}
}
}Prompts to try
Example prompts that exercise the server's econometric and statistical analysis capabilities.
- "Run a linear regression of sales on marketing_spend and store_size from this CSV data"
- "Perform a panel data fixed-effects regression with entity and time fixed effects"
- "Test whether GDP growth and unemployment follow Okun's Law using instrumental variables"
- "Fit an ARIMA model to this monthly revenue time series and forecast the next 12 months"
- "Run a Hausman test to choose between fixed and random effects for my panel model"
- "Cluster these 500 customers into 5 groups using k-means and describe each cluster"Troubleshooting R Econometrics
rmcp cannot find R on startup
Ensure R is installed and the `Rscript` binary is on your system PATH. Test with `Rscript --version`. On macOS with Homebrew, R is typically at /usr/local/bin/Rscript. You may need to restart your terminal after installing R.
R package not found errors when running models
Some tools require packages not in the base R installation. Run `install.packages('package_name')` in an R session for any missing package. For caret-based models, also run `install.packages('caret')` and its dependencies.
Analysis times out for large datasets
Increase the timeout by setting RMCP_R_TIMEOUT in your environment (e.g., 300 for 5 minutes). For very large datasets, consider sampling or pre-aggregating the data before passing it to the model.
Frequently Asked Questions about R Econometrics
What is R Econometrics?
R Econometrics is a Model Context Protocol (MCP) server that provides econometric modeling capabilities through r, enabling ai assistants to perform sophisticated analyses including linear regression, panel data models, instrumental variables regression, and diagnostic tests. It connects AI assistants to external tools and data sources through a standardized interface.
How do I install R Econometrics?
Follow the installation instructions on the R Econometrics GitHub repository. Clone the repo, install dependencies, and add the server config to your AI client.
Which AI clients work with R Econometrics?
R Econometrics works with all major MCP-compatible AI clients including Claude Desktop, Claude Code, Cursor, VS Code (GitHub Copilot), Windsurf, and Cline.
Is R Econometrics free to use?
Yes, R Econometrics is open source and available under the MIT License license. You can use it freely in both personal and commercial projects.
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Set Up R Econometrics in Your Editor
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Quick Config Preview
Add this to your claude_desktop_config.json or .cursor/mcp.json
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