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 (OLS) with robust standard errors, R², and p-values. Parameters: formula (string), data (dictionary)
Conduct binary classification analysis with logistic regression, providing odds ratios and accuracy. Parameters: formula (string), data (dictionary), family (string), link (string)
Analyze the correlation between two variables using Pearson, Spearman, or Kendall methods. Parameters: data (dictionary), variables (list of strings), method (string)
Create and fit an ARIMA model for time series forecasting. Parameters: order (tuple), seasonal_order (tuple), data (array)
Decompose a time series into its trend, seasonal, and remainder components. Parameters: data (array), model (string)
Test the stationarity of a time series using ADF, KPSS, or Phillips-Perron tests. Parameters: data (array), test_type (string)
Create lagged or lead variables for time series analysis. Parameters: data (array), lag (integer), lead (integer)
Cap extreme values to handle outliers in the dataset. Parameters: data (array), limits (tuple)
Create a stationary series by differencing the data. Parameters: data (array), lag (integer)
Standardize data using Z-score, min-max, or robust scaling methods. Parameters: data (array), method (string)
Conduct one-sample, two-sample, or paired t-tests to compare means. Parameters: data (array), group (string), type (string)
Perform analysis of variance (ANOVA) to compare means across multiple groups. Parameters: data (array), formula (string)
Conduct chi-square tests for independence or goodness-of-fit. Parameters: observed (array), expected (array)
Test for normality in a dataset using Shapiro-Wilk, Jarque-Bera, or Anderson-Darling tests. Parameters: data (array), test_type (string)
Generate comprehensive descriptive statistics for the dataset. Parameters: data (array), group (string)
Detect outliers in the dataset using IQR, Z-score, or Modified Z-score methods. Parameters: data (array), method (string)
Create frequency tables including counts and percentages with optional sorting. Parameters: data (array), sort (boolean)
Perform panel regression analysis using fixed or random effects for longitudinal data. Parameters: data (array), model_type (string)
Conduct instrumental variables regression using 2SLS with endogeneity testing. Parameters: data (array), instruments (array)
Fit a vector autoregression model for multivariate time series analysis. Parameters: data (array), lags (integer)
Perform K-Means clustering for unsupervised data classification with validation options. Parameters: data (array), n_clusters (integer)
Build classification or regression trees for predictive modeling. Parameters: data (array), target (string), criterion (string)
Implement random forest algorithms for ensemble learning and variable importance assessment. Parameters: data (array), target (string), n_estimators (integer)
Generate scatter plots to visualize correlation between two variables with trend lines. Parameters: x (array), y (array), title (string)
Create histograms to analyze the distribution of a dataset with optional density overlay. Parameters: data (array), bins (integer)
Generate box plots for quartile analysis and outlier detection. Parameters: data (array), title (string)
Visualize temporal data using time series plots. Parameters: data (array), title (string)
Create heatmaps to visualize correlation matrices. Parameters: data (array), title (string)
Generate regression diagnostics plots for model validation. Parameters: model (object), title (string)
Import data from CSV files with flexible parsing options. Parameters: filepath (string), options (dictionary)
Export data to CSV files with formatting control. Parameters: filepath (string), data (array), options (dictionary)
Retrieve comprehensive information about the dataset structure. Parameters: data (array)
Select data conditionally based on complex criteria. Parameters: data (array), conditions (dictionary)
