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 Ordinary Least Squares (OLS) regression with robust standard errors, R², and p-values. Parameters: formula (string), data (dictionary)
Conduct binary classification with logistic regression, providing odds ratios and accuracy. Parameters: formula (string), data (dictionary), family (string, optional), link (string, optional)
Analyze correlation using Pearson, Spearman, or Kendall methods. Parameters: data (dictionary), variables (list of strings), method (string)
Fit an Autoregressive Integrated Moving Average (ARIMA) model for forecasting. Parameters: data (dictionary), order (tuple of integers)
Decompose a time series into trend, seasonal, and remainder components. Parameters: data (dictionary), model (string, optional)
Test for stationarity using ADF, KPSS, or Phillips-Perron tests. Parameters: data (dictionary), test_type (string)
Create lagged or lead variables for time series analysis. Parameters: data (dictionary), lag (integer), lead (integer)
Handle outliers by capping extreme values in the dataset. Parameters: data (dictionary), limits (tuple of floats)
Create a differenced series to achieve stationarity in time series analysis. Parameters: data (dictionary), lag (integer)
Standardize data using Z-score, min-max, or robust scaling methods. Parameters: data (dictionary), method (string)
Perform one-sample, two-sample, or paired t-tests. Parameters: data (dictionary), test_type (string)
Conduct Analysis of Variance (ANOVA) with Types I, II, or III. Parameters: data (dictionary), model (string)
Perform chi-square tests for independence or goodness-of-fit. Parameters: data (dictionary), test_type (string)
Test for normality using Shapiro-Wilk, Jarque-Bera, or Anderson-Darling tests. Parameters: data (dictionary), test_type (string)
Generate comprehensive summary statistics for the dataset, including grouping options. Parameters: data (dictionary), group_by (optional list of strings)
Detect outliers using IQR, Z-score, or Modified Z-score methods. Parameters: data (dictionary), method (string)
Create frequency tables showing counts and percentages, with sorting options. Parameters: data (dictionary), variable (string)
Perform fixed or random effects regression on longitudinal panel data. Parameters: data (dictionary), model (string)
Conduct instrumental variables regression using Two-Stage Least Squares (2SLS) with endogeneity testing. Parameters: data (dictionary), instruments (list of strings)
Fit a Vector Autoregression (VAR) model for multivariate time series analysis. Parameters: data (dictionary), lags (integer)
Perform K-Means clustering for unsupervised learning, including validation metrics. Parameters: data (dictionary), n_clusters (integer)
Create classification or regression trees using decision tree algorithms. Parameters: data (dictionary), target (string)
Use ensemble methods to create Random Forest models with variable importance metrics. Parameters: data (dictionary), target (string), n_estimators (integer)
Generate scatter plots to visualize correlations, including trend lines. Parameters: data (dictionary), x (string), y (string)
Create histograms to analyze data distribution, with an optional density overlay. Parameters: data (dictionary), variable (string)
Generate box plots for quartile analysis and outlier detection. Parameters: data (dictionary), variable (string)
Visualize temporal data through time series plots. Parameters: data (dictionary), time (string), value (string)
Create heatmaps to visualize correlation matrices. Parameters: data (dictionary), variables (list of strings)
Generate plots for regression diagnostics to validate model assumptions. Parameters: model (object)
Import CSV files into the server with flexible parsing options. Parameters: filepath (string), options (optional dictionary)
Export datasets to CSV format with control over formatting options. Parameters: filepath (string), data (dictionary), options (optional dictionary)
Obtain comprehensive information about the dataset structure. Parameters: data (dictionary)
Select data based on complex conditional criteria. Parameters: data (dictionary), conditions (dictionary)
