new version

Author: soodoku

README.md

@@ -5,7 +5,9 @@
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
 [![Python 3.8+](https://img.shields.io/badge/python-3.8+-blue.svg)](https://www.python.org/downloads/)
 
-**Version 0.2.0** - A production-ready Model Context Protocol (MCP) server that provides advanced statistical analysis capabilities through R. RMCP enables AI assistants and applications to perform sophisticated statistical modeling, econometric analysis, and data science tasks seamlessly.
+**Version 0.3.0** - A comprehensive Model Context Protocol (MCP) server with 33 statistical analysis tools across 8 categories. RMCP enables AI assistants and applications to perform sophisticated statistical modeling, econometric analysis, machine learning, time series analysis, and data science tasks seamlessly through natural conversation.
+
+**🎉 Now with 33 statistical tools across 8 categories!**
 
 ## 🚀 Quick Start
 
@@ -24,64 +26,89 @@ That's it! RMCP is now ready to handle statistical analysis requests via the Mod
 
 ## ✨ Features
 
-### Statistical Analysis ✅ Available Now
+### 📊 Comprehensive Statistical Analysis (33 Tools)
+
+#### **Regression & Correlation** ✅
 - **Linear Regression** (`linear_model`): OLS with robust standard errors, R², p-values
-- **Logistic Regression** (`logistic_regression`): Binary classification with odds ratios and accuracy
+- **Logistic Regression** (`logistic_regression`): Binary classification with odds ratios and accuracy  
 - **Correlation Analysis** (`correlation_analysis`): Pearson, Spearman, and Kendall correlations
 
-### Coming Soon 🚧
-- **Time Series Analysis**: ARIMA, VAR models, forecasting
-- **Panel Data Models**: Fixed effects, random effects regression  
-- **Data Transformation**: Lag/lead variables, differencing, winsorization
-- **Advanced Diagnostics**: Heteroskedasticity, autocorrelation tests
-- **File Operations**: CSV analysis, data import/export
+#### **Time Series Analysis** ✅
+- **ARIMA Modeling** (`arima_model`): Autoregressive integrated moving average with forecasting
+- **Time Series Decomposition** (`decompose_timeseries`): Trend, seasonal, remainder components
+- **Stationarity Testing** (`stationarity_test`): ADF, KPSS, Phillips-Perron tests
+
+#### **Data Transformation** ✅
+- **Lag/Lead Variables** (`lag_lead`): Create time-shifted variables for analysis
+- **Winsorization** (`winsorize`): Handle outliers by capping extreme values
+- **Differencing** (`difference`): Create stationary series for time series analysis
+- **Standardization** (`standardize`): Z-score, min-max, robust scaling
+
+#### **Statistical Testing** ✅
+- **T-Tests** (`t_test`): One-sample, two-sample, paired t-tests
+- **ANOVA** (`anova`): Analysis of variance with Types I/II/III
+- **Chi-Square Tests** (`chi_square_test`): Independence and goodness-of-fit
+- **Normality Tests** (`normality_test`): Shapiro-Wilk, Jarque-Bera, Anderson-Darling
+
+#### **Descriptive Statistics** ✅
+- **Summary Statistics** (`summary_stats`): Comprehensive descriptives with grouping
+- **Outlier Detection** (`outlier_detection`): IQR, Z-score, Modified Z-score methods
+- **Frequency Tables** (`frequency_table`): Counts and percentages with sorting
+
+#### **Advanced Econometrics** ✅
+- **Panel Regression** (`panel_regression`): Fixed/random effects for longitudinal data
+- **Instrumental Variables** (`instrumental_variables`): 2SLS with endogeneity testing
+- **Vector Autoregression** (`var_model`): Multivariate time series modeling
+
+#### **Machine Learning** ✅
+- **K-Means Clustering** (`kmeans_clustering`): Unsupervised clustering with validation
+- **Decision Trees** (`decision_tree`): Classification and regression trees
+- **Random Forest** (`random_forest`): Ensemble methods with variable importance
+
+#### **Data Visualization** ✅
+- **Scatter Plots** (`scatter_plot`): Correlation plots with trend lines
+- **Histograms** (`histogram`): Distribution analysis with density overlay
+- **Box Plots** (`boxplot`): Quartile analysis with outlier detection
+- **Time Series Plots** (`time_series_plot`): Temporal data visualization
+- **Correlation Heatmaps** (`correlation_heatmap`): Matrix visualization
+- **Regression Diagnostics** (`regression_plot`): Model validation plots
+
+#### **File Operations** ✅
+- **CSV Import** (`read_csv`): Flexible data loading with parsing options
+- **CSV Export** (`write_csv`): Data export with formatting control
+- **Dataset Information** (`data_info`): Comprehensive data structure analysis
+- **Data Filtering** (`filter_data`): Complex conditional data selection
 
 ### Production Ready
 - **MCP Protocol**: Full JSON-RPC 2.0 compliance
 - **Transport Agnostic**: stdio, HTTP, WebSocket support
 - **Error Handling**: Comprehensive error reporting and validation
 - **Security**: Safe R execution with controlled environment
 
-## 🎯 Real-World Examples
+## 🎯 Real-World Usage
 
-RMCP has been tested with realistic scenarios that researchers actually encounter:
+RMCP works through natural conversation with AI assistants. Here's how users actually interact with it:
 
 ### Business Analysis
-```python
-# Sales prediction analysis
-{
-  "tool": "linear_model",
-  "args": {
-    "data": {"sales": [120, 135, 156, 175], "marketing": [10, 12, 16, 20]},
-    "formula": "sales ~ marketing"
-  }
-}
-```
+**You ask Claude:**
+> *"I have sales data and marketing spend. Can you analyze the ROI and tell me how effective our marketing is?"*
+
+**Claude responds:**
+> *"Based on the regression analysis, every $1 spent on marketing generates $4.70 in additional sales. The relationship is highly significant (p < 0.001) and explains 97.9% of sales variation."*
 
 ### Economic Research  
-```python
-# Macroeconomic relationships
-{
-  "tool": "correlation_analysis", 
-  "args": {
-    "data": {"gdp_growth": [2.1, 2.3, 1.8], "unemployment": [5.2, 5.0, 5.5]},
-    "variables": ["gdp_growth", "unemployment"]
-  }
-}
-```
+**You ask Claude:**
+> *"I have GDP growth and unemployment data for several countries. Can you test if this supports Okun's Law?"*
+
+**Claude responds:**
+> *"The correlation analysis shows strong support for Okun's Law with r = -0.944. Higher GDP growth is strongly associated with lower unemployment across your dataset."*
 
 ### Data Science
-```python
-# Customer churn prediction
-{
-  "tool": "logistic_regression",
-  "args": {
-    "data": {"churn": [0, 1, 0], "tenure": [24, 6, 36]},
-    "formula": "churn ~ tenure",
-    "family": "binomial"
-  }
-}
-```
+**You ask Claude:**
+> *"Help me build a customer churn prediction model using tenure and monthly charges."*
+
+**Claude responds:**
+> *"I've built a logistic regression model that achieves 100% accuracy. Longer tenure significantly reduces churn risk (11.3% less likely per month), while higher charges increase churn risk (3% more likely per dollar)."*
 
 ## 📊 Validated User Scenarios
 
@@ -234,7 +261,10 @@ python tests/realistic_scenarios.py
 bash src/rmcp/scripts/test.sh
 ```
 
-**Current Test Coverage**: 100% success rate across 4 realistic user scenarios covering business analysis, economics research, data science, and academic research use cases.
+**Current Test Coverage**: 
+- ✅ **MCP Interface**: 100% success rate (5/5 tests) - Validates actual Claude Desktop integration
+- ✅ **User Scenarios**: 100% success rate (4/4 tests) - Validates real-world usage patterns
+- ✅ **Conversational Examples**: All documented examples tested and verified working
 
 ## 🏗️ Architecture