Rmcp

An econometric model protocol server based on the R language, providing advanced statistical analysis and econometric modeling functions.

Research and data Developer tools #Econometrics #Statistical Analysis #R Language #Data Modeling .Python

rating : 2.5 points

downloads : 15.4K

update time : 2025-04-29

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What is the R MCP Server?

The R MCP Server is a tool that provides advanced econometric models and data analysis functions through the Model Context Protocol (MCP). It allows users to run various statistical analyses such as linear regression, panel data analysis, and instrumental variable regression, and generate detailed diagnostic reports.

How to use the R MCP Server?

Users can send JSON - formatted requests to the server to obtain corresponding analysis results. The server supports multiple statistical models and provides rich diagnostic test functions.

Applicable Scenarios

The R MCP Server is very suitable for researchers and analysts who need to conduct complex economic data analysis, especially when dealing with linear regression, panel data, and causal inference.

Main Features

Linear Regression

Supports running standard linear regression models, and users can choose whether to use robust standard errors.

Panel Data Analysis

Provides multiple panel data models such as fixed - effect, random - effect, pooled regression, between - group estimation, and difference methods.

Instrumental Variable Regression

Used to solve endogeneity problems, build and estimate instrumental variable regression models.

Diagnostic Tests

Check whether the model has problems such as heteroscedasticity and autocorrelation, and provide suggested correction methods.

Descriptive Statistics

Generate basic statistics of the dataset, such as mean and standard deviation.

Correlation Analysis

Calculate the Pearson or Spearman correlation coefficients between variables.

Group Aggregation

Group the data according to the specified columns and calculate summary statistics.

Advantages

Powerful econometric modeling capabilities.

Supports multiple diagnostic tests to ensure model quality.

Easy to integrate into existing workflows.

Open - source and free to use.

Limitations

Requires a certain programming foundation to operate JSON requests.

May have limited performance for large - scale data.

Some advanced functions depend on the installation of specific R packages.

How to Use

Install the R MCP Server

Download and install Docker, then build and start the container.

Send a JSON Request

Write a JSON - formatted request file and pass it to the server.

Usage Examples

Linear Regression Example

Analyze the relationship between price and automobile fuel efficiency.

Panel Data Analysis Example

Evaluate the applicability of fixed - effect or random - effect models.

Instrumental Variable Regression Example

Set up instrumental variable regression to solve endogeneity problems.

Frequently Asked Questions

How to install the R MCP Server?

How to write a JSON request?

How to check whether the model has heteroscedasticity?

Related Resources

R MCP Server Documentation

Official documentation and guides.

R Language Tutorial

Learn the basic knowledge of the R language.

Introduction to Econometrics

Understand the basic concepts of econometrics.

🚀 R MCP Server

An R-implemented Model Context Protocol (MCP) server that offers advanced econometric modeling and data analysis capabilities. This server enables AI assistants to seamlessly perform complex econometric and statistical analyses, helping you quickly gain insights from data.

🚀 Quick Start

The R MCP Server is designed to facilitate complex econometric and statistical analyses. It allows AI assistants to interact with the R environment to run various models and tests.

✨ Features

Linear Regression: Run linear models with optional robust standard errors.
Panel Data Analysis: Estimate fixed effects, random effects, pooling, between effects, and first-difference models.
Instrumental Variables: Build and estimate IV regression models.
Diagnostic Tests: Test for heteroscedasticity, autocorrelation, and model misspecification.
Descriptive Statistics: Generate summary statistics of datasets using R's summary() function.
Correlation Analysis: Calculate Pearson or Spearman correlation coefficients between variables.
Group Aggregation: Group data by specified columns and calculate summary statistics using dplyr.
Resources: Access reference documentation for various econometric techniques.
Prompts: Execute common econometric analyses using predefined prompt templates.

📦 Installation

Using Docker (Recommended)

Build the Docker image:
```
docker build -t r-econometrics-mcp .
```
Run the container:
```
docker run -it r-econometrics-mcp
```

Manual Installation

Install the required Python packages:

pip install -r requirements.txt

Install the required R packages:

install.packages(c("plm", "lmtest", "sandwich"))

💻 Usage Examples

Basic Usage

The server provides the following tools:

linear_model

Run a linear regression model.

Parameters:

formula (string): Regression formula (e.g., 'y ~ x1 + x2')
data (object): Dataset as a dictionary/JSON object
robust (boolean, optional): Whether to use robust standard errors

panel_model

Run a panel data model.

Parameters:

formula (string): Regression formula (e.g., 'y ~ x1 + x2')
data (object): Dataset as a dictionary/JSON object
index (array): Panel index variables (e.g., ['individual', 'time'])
effect (string, optional): Effect type: 'individual', 'time', or 'twoways'
model (string, optional): Model type: 'within', 'random', 'pooling', 'between', or 'fd'