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 using ordinary least squares with robust standard errors, R², and p-values.
Conduct binary classification using logistic regression, providing odds ratios and accuracy.
Analyze correlation between variables using Pearson, Spearman, and Kendall methods.
Perform ARIMA modeling for time series analysis and forecasting.
Decompose time series data into trend, seasonal, and remainder components.
Conduct stationarity testing using ADF, KPSS, and Phillips-Perron tests.
Create lagged or lead variables for time series analysis.
Handle outliers by capping extreme values in the dataset.
Create stationary series for time series analysis through differencing.
Standardize data using Z-score, min-max, or robust scaling methods.
Perform one-sample, two-sample, and paired t-tests.
Conduct analysis of variance with Types I, II, and III.
Perform Chi-Square tests for independence and goodness-of-fit.
Conduct normality tests including Shapiro-Wilk, Jarque-Bera, and Anderson-Darling.
Generate comprehensive summary statistics with grouping.
Detect outliers using IQR, Z-score, and Modified Z-score methods.
Create frequency tables with counts and percentages, sorted as required.
Perform panel regression analysis with fixed or random effects for longitudinal data.
Conduct instrumental variable analysis using 2SLS with endogeneity testing.
Perform vector autoregression for multivariate time series modeling.
Execute k-means clustering as an unsupervised learning technique with validation.
Build classification and regression decision trees.
Apply random forest methods for ensemble learning and assess variable importance.
Create scatter plots to visualize correlations with trend lines.
Generate histograms for distribution analysis with density overlay.
Create box plots to analyze quartiles and detect outliers.
Visualize temporal data through time series plots.
Generate heatmaps to visualize correlations in a matrix format.
Create regression diagnostics plots for model validation.
Import CSV files with flexible data loading and parsing options.
Export datasets to CSV format with options for formatting control.
Analyze and provide comprehensive information about the data structure.
Select complex conditional data subsets through data filtering.
