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 using logistic regression with odds ratios and accuracy. Parameters: formula (string), data (dictionary), family (optional string), link (optional string)
Analyze correlation using Pearson, Spearman, or Kendall methods. Parameters: data (dictionary), variables (list of strings), method (string)
Model time series data using Autoregressive Integrated Moving Average (ARIMA) with forecasting capabilities. Parameters: data (dictionary), order (tuple), seasonal_order (optional tuple)
Decompose time series data into trend, seasonal, and remainder components. Parameters: data (dictionary)
Perform tests for stationarity on time series data, including ADF, KPSS, and Phillips-Perron tests. Parameters: data (dictionary), test_type (string)
Create lagged or lead variables for time series analysis. Parameters: data (dictionary), lag (int), lead (int)
Handle outliers in data by capping extreme values. Parameters: data (dictionary), limits (tuple)
Create a stationary series for time series analysis by differencing. Parameters: data (dictionary), lag (int)
Standardize data using Z-score, min-max, or robust scaling. Parameters: data (dictionary), method (string)
Perform one-sample, two-sample, or paired t-tests. Parameters: data (dictionary), test_type (string)
Conduct analysis of variance with Types I/II/III. Parameters: data (dictionary), factors (list of strings)
Perform Chi-Square tests for independence and goodness-of-fit. Parameters: data (dictionary), test_type (string)
Conduct normality tests including Shapiro-Wilk, Jarque-Bera, and Anderson-Darling tests. Parameters: data (dictionary), test_type (string)
Generate comprehensive summary statistics with grouping. 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 with counts and percentages, sorted accordingly. Parameters: data (dictionary), variable (string)
Conduct panel regression analysis with fixed or random effects for longitudinal data. Parameters: data (dictionary), model_type (string)
Perform two-stage least squares (2SLS) regression with endogeneity testing. Parameters: data (dictionary), instruments (list of strings)
Model multivariate time series data using Vector Autoregression. Parameters: data (dictionary), lags (int)
Perform K-Means clustering for unsupervised learning with validation. Parameters: data (dictionary), n_clusters (int)
Build classification and regression trees for predictive modeling. Parameters: data (dictionary), target (string), features (list of strings)
Employ ensemble methods using Random Forest for classification or regression. Parameters: data (dictionary), target (string), features (list of strings)
Generate scatter plots to visualize correlation with trend lines. Parameters: data (dictionary), x (string), y (string)
Create histograms for distribution analysis with density overlay. Parameters: data (dictionary), variable (string)
Produce 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)
Generate heatmaps for visualizing correlation matrices. Parameters: data (dictionary), variables (list of strings)
Create diagnostic plots for model validation in regression analysis. Parameters: data (dictionary), model (string)
Import data from CSV files with flexible parsing options. Parameters: file_path (string), options (optional dictionary)
Export data to CSV files with various formatting controls. Parameters: file_path (string), data (dictionary), options (optional dictionary)
Provide comprehensive data structure analysis, including types and null values. Parameters: data (dictionary)
Perform complex conditional data selection based on provided criteria. Parameters: data (dictionary), conditions (dictionary)
