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 OLS regression with robust standard errors, R², and p-values.
Conduct binary classification with odds ratios and accuracy.
Calculate Pearson, Spearman, and Kendall correlations between variables.
Fit an autoregressive integrated moving average model for time series forecasting.
Decompose time series into trend, seasonal, and remainder components.
Perform ADF, KPSS, and Phillips-Perron tests to check for stationarity.
Create time-shifted variables for analysis.
Handle outliers by capping extreme values.
Create stationary series for time series analysis.
Standardize data using Z-score, min-max, or robust scaling.
Conduct one-sample, two-sample, and paired t-tests.
Perform analysis of variance with Types I/II/III.
Conduct independence and goodness-of-fit tests using chi-square.
Test for normality using Shapiro-Wilk, Jarque-Bera, and Anderson-Darling tests.
Generate comprehensive descriptive statistics with grouping.
Detect outliers using IQR, Z-score, and Modified Z-score methods.
Create frequency tables with counts and percentages.
Conduct fixed or random effects regression for longitudinal data.
Perform 2SLS regression with endogeneity testing.
Model multivariate time series using vector autoregression.
Perform unsupervised clustering and validation using K-Means.
Build classification and regression trees.
Implement ensemble methods for classification and regression with variable importance.
Create correlation scatter plots with trend lines.
Visualize data distribution with histograms and density overlays.
Analyze quartiles and detect outliers using box plots.
Visualize temporal data through time series plots.
Create matrix visualizations of correlation between variables.
Generate model validation plots for regression analysis.
Import data from CSV files with flexible parsing options.
Export data to CSV files with formatting control.
Provide comprehensive analysis of the data structure.
Select data based on complex conditional statements.
