An R-based econometrics MCP server that provides advanced statistical modeling capabilities through R packages like plm, lmtest, and AER. Developed by goji+, the server enables AI assistants to perform complex econometric analyses including linear regression, panel data modeling, instrumental variables regression, and diagnostic testing across various research domains. Useful for researchers and data scientists seeking programmatic access to robust statistical modeling tools.
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Highlight AIPerform linear regression analysis with OLS, robust standard errors, R², and p-values.
Conduct binary classification using logistic regression, providing odds ratios and accuracy.
Analyze correlations using Pearson, Spearman, and Kendall methods.
Fit an ARIMA model for time series forecasting.
Decompose a time series into trend, seasonal, and remainder components.
Test for stationarity in time series data using ADF, KPSS, or Phillips-Perron tests.
Create lag or lead variables for time-shifted analysis.
Handle outliers by capping extreme values in the dataset.
Create stationary series for time series analysis by differencing.
Standardize data using Z-score, min-max, or robust scaling methods.
Perform one-sample, two-sample, or paired t-tests.
Conduct analysis of variance with Types I/II/III options.
Perform chi-square tests for independence and goodness-of-fit.
Test for normality using Shapiro-Wilk, Jarque-Bera, or Anderson-Darling tests.
Generate comprehensive descriptive statistics with grouping.
Detect outliers using IQR, Z-score, or Modified Z-score methods.
Create frequency tables with counts and percentages.
Conduct fixed or random effects panel regression for longitudinal data.
Apply 2SLS for instrumental variable analysis with endogeneity testing.
Model multivariate time series using Vector Autoregression.
Perform unsupervised clustering using K-Means algorithm with validation.
Build classification or regression trees.
Implement ensemble methods using Random Forest for classification and regression.
Create scatter plots to visualize correlation with trend lines.
Generate histograms for distribution analysis with density overlay.
Create box plots for quartile analysis and outlier detection.
Visualize temporal data through time series plots.
Generate matrix visualizations for correlation analysis.
Produce model validation plots for regression diagnostics.
Import CSV files with flexible data loading and parsing options.
Export data to CSV with formatting control.
Analyze and provide comprehensive information about the dataset structure.
Perform complex conditional data selection.
