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most comprehensive tabular data analysis package for Vue.js - Advanced AI, ML, Statistical Analysis, and Data Science capabilities","maintainers":[{"name":"aivue","email":"reachbrt@gmail.com"}],"readme":"# @aivue/tabular-intelligence\n\n> **The Most Comprehensive Tabular Data Analysis Package for Vue.js**\n> Advanced AI, ML, Statistical Analysis, and Data Science capabilities in one powerful package\n\n[![npm version](https://img.shields.io/npm/v/@aivue/tabular-intelligence.svg)](https://www.npmjs.com/package/@aivue/tabular-intelligence)\n[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)\n[![Downloads](https://img.shields.io/npm/dm/@aivue/tabular-intelligence.svg)](https://www.npmjs.com/package/@aivue/tabular-intelligence)\n\n## 🚀 What's New in v2.0\n\n**Tabular Intelligence is now a complete data science toolkit!** We've added 15+ advanced features that make it completely different from @aivue/smart-datatable:\n\n- 📊 **Data Quality Profiling** - Comprehensive data quality assessment and profiling\n- 🧹 **Smart Data Cleaning** - Intelligent missing value imputation and outlier handling\n- 🔧 **Feature Engineering** - Automated feature generation and selection\n- ⏰ **Time Series Analysis** - Forecasting, trend detection, seasonality analysis\n- 🤖 **AutoML** - Automated model selection and hyperparameter tuning\n- 🔍 **Model Explainability** - SHAP values, feature importance, counterfactuals\n- 📈 **Statistical Testing** - A/B testing, hypothesis testing, significance tests\n- 📊 **Visualization Recommendations** - Smart chart suggestions based on data\n- 🔗 **Multi-Table Analysis** - Table joins, relationship detection, cross-table queries\n- 📝 **Auto Reporting** - Generate comprehensive insights and reports\n- 🔒 **Privacy & Compliance** - PII detection, anonymization, GDPR/CCPA compliance\n- 📦 **Data Versioning** - Snapshots, lineage tracking, transformation pipelines\n- 🌊 **Streaming Data** - Real-time data processing and monitoring\n- 🎯 **Smart Sampling** - Intelligent data sampling strategies\n\n## 🎯 Core Features\n\n### 🔌 Foundation & Integration\n- **Generic TFM Client** - Connect to any HTTP-based Tabular Foundation Model API\n- **Natural Language Q&A** - Ask questions about your data in plain English\n- **Postman Collection Integration** - Import Postman collections and query API data with AI\n- **Table Extraction** - Extract data from HTML tables or Vue data grids\n- **Local Fallback** - Built-in statistical analysis when API is unavailable\n- **Vue Integration** - Reactive composables for seamless Vue.js integration\n\n### 📊 Statistical Analysis\n- **Descriptive Statistics** - Mean, median, mode, std dev, percentiles, distributions\n- **Anomaly Detection** - Statistical and ML-based outlier detection\n- **Segmentation & Clustering** - K-means, DBSCAN, hierarchical clustering\n- **Predictions** - Time series forecasting and predictive modeling\n- **Correlation Analysis** - Pearson correlation matrices and significance testing\n- **AI Summaries** - Generate intelligent summaries of your data\n\n### 💬 Q&A Capabilities\n\n**Ask any question about your Vue tables – AI answers directly from your data.**\n\n- \"Which region had the highest revenue last quarter?\"\n- \"How many customers churned with tenure < 6 months?\"\n- \"What is the average order value for India vs US?\"\n- \"Show me products with price > $100 and quantity < 10\"\n\nThe AI analyzes your table data and provides natural language answers with supporting statistics and data.\n\n### 📮 Postman Collection Integration\n\n**Import Postman collections and query API data with natural language.**\n\n- Import Postman Collection v2.1 JSON files\n- Automatically discover all API endpoints\n- Execute API requests with variable substitution\n- Convert API responses to tabular format\n- Ask questions about API data using AI\n\nPerfect for MarketStack, financial APIs, and any REST API with tabular data!\n\nSee [POSTMAN-INTEGRATION.md](./POSTMAN-INTEGRATION.md) for detailed documentation.\n\n## 📦 Installation\n\n```bash\n# npm\nnpm install @aivue/tabular-intelligence @aivue/core\n\n# yarn\nyarn add @aivue/tabular-intelligence @aivue/core\n\n# pnpm\npnpm add @aivue/tabular-intelligence @aivue/core\n```\n\n### 🔄 Vue Compatibility\n\n- **✅ Vue 2**: Compatible with Vue 2.6.0 and higher\n- **✅ Vue 3**: Compatible with all Vue 3.x versions\n\n> The package automatically detects which version of Vue you're using and provides the appropriate compatibility layer. This means you can use the same package regardless of whether your project is using Vue 2 or Vue 3.\n\n## 🚀 Quick Start\n\n### Basic Usage (Local Mode)\n\n```typescript\nimport { useTabularIntelligence } from '@aivue/tabular-intelligence';\n\n// Local mode - no API required\nconst { analyze, getDescriptiveStats, detectAnomalies } = useTabularIntelligence({\n  config: {\n    provider: 'local',\n    baseUrl: '',\n  },\n  data: ref(yourData),\n});\n\n// Get descriptive statistics\nconst stats = await getDescriptiveStats();\n\n// Detect anomalies\nconst anomalies = await detectAnomalies(['price', 'quantity'], 0.7);\n\n// Perform clustering\nconst clusters = await performClustering(['feature1', 'feature2'], 3);\n```\n\n### With TabPFN (Prior Labs)\n\n```typescript\nimport { useTabularIntelligence } from '@aivue/tabular-intelligence';\n\nconst { analyze } = useTabularIntelligence({\n  config: {\n    provider: 'tabpfn',\n    baseUrl: 'https://api.priorlabs.ai/v1/predict',\n    apiKey: 'your-tabpfn-api-key', // Get from https://priorlabs.ai/\n  },\n  data: ref(yourData),\n  useLocalFallback: true, // Fallback to local if API fails\n});\n```\n\n### With Custom TFM API\n\n```typescript\nimport { useTabularIntelligence } from '@aivue/tabular-intelligence';\n\nconst { analyze } = useTabularIntelligence({\n  config: {\n    provider: 'custom',\n    baseUrl: 'https://your-tfm-api.com/analyze',\n    apiKey: 'your-api-key',\n  },\n  data: ref(yourData),\n  useLocalFallback: true,\n});\n```\n\n### With Smart DataTable\n\n```vue\n<template>\n  <div>\n    <SmartDataTable :data=\"tableData\" :columns=\"columns\" />\n    \n    <button @click=\"analyzeData\">Analyze Data</button>\n    \n    <div v-if=\"anomalies.length\">\n      <h3>Anomalies Detected: {{ anomalies.length }}</h3>\n      <div v-for=\"anomaly in anomalies\" :key=\"anomaly.rowIndex\">\n        Row {{ anomaly.rowIndex }}: {{ anomaly.reasons.join(', ') }}\n      </div>\n    </div>\n  </div>\n</template>\n\n<script setup>\nimport { ref } from 'vue';\nimport { SmartDataTable } from '@aivue/smart-datatable';\nimport { useTabularIntelligence } from '@aivue/tabular-intelligence';\n\nconst tableData = ref([\n  { id: 1, name: 'Product A', price: 100, quantity: 50 },\n  { id: 2, name: 'Product B', price: 200, quantity: 30 },\n  { id: 3, name: 'Product C', price: 9999, quantity: 1 }, // Anomaly!\n]);\n\nconst { detectAnomalies } = useTabularIntelligence({\n  config: {\n    provider: 'custom',\n    baseUrl: 'https://api.example.com/tfm',\n  },\n  data: tableData,\n  useLocalFallback: true,\n});\n\nconst anomalies = ref([]);\n\nasync function analyzeData() {\n  anomalies.value = await detectAnomalies(['price', 'quantity']);\n}\n</script>\n```\n\n## 💬 Q&A Usage\n\n### Setup Q&A\n\n```typescript\nimport { useTabularIntelligence, QuestionInput, AnswerDisplay, QuestionHistory } from '@aivue/tabular-intelligence';\n\nconst {\n  askQuestion,\n  generateSummary,\n  questionHistory,\n  lastAnswer,\n  clearHistory,\n} = useTabularIntelligence({\n  config: {\n    provider: 'local',\n    baseUrl: 'https://api.example.com/tfm',\n  },\n  data: tableData,\n  qaConfig: {\n    provider: 'openai',\n    apiKey: 'sk-...',\n    model: 'gpt-4-turbo-preview',\n  },\n});\n\n// Ask a question\nconst answer = await askQuestion('What is the average price by category?');\n\n// Generate AI summary\nconst summary = await generateSummary();\n```\n\n### Q&A Components\n\n```vue\n<template>\n  <div>\n    <!-- Question Input -->\n    <QuestionInput\n      :loading=\"loading\"\n      @submit=\"handleQuestion\"\n    />\n\n    <!-- Latest Answer -->\n    <AnswerDisplay\n      v-if=\"lastAnswer\"\n      :answer=\"lastAnswer\"\n    />\n\n    <!-- Question History -->\n    <QuestionHistory\n      :questions=\"questionHistory\"\n      @clear=\"clearHistory\"\n      @select=\"handleSelectQuestion\"\n    />\n  </div>\n</template>\n\n<script setup>\nimport { QuestionInput, AnswerDisplay, QuestionHistory, useTabularIntelligence } from '@aivue/tabular-intelligence';\n\nconst { askQuestion, questionHistory, lastAnswer, clearHistory } = useTabularIntelligence({\n  config: { provider: 'local' },\n  data: tableData,\n  qaConfig: {\n    provider: 'openai',\n    apiKey: import.meta.env.VITE_OPENAI_API_KEY,\n  },\n});\n\nasync function handleQuestion(question) {\n  await askQuestion(question);\n}\n\nfunction handleSelectQuestion(question) {\n  console.log('Selected:', question);\n}\n</script>\n```\n\n### Table Extraction\n\nExtract data from HTML tables or Vue data grids:\n\n```typescript\n// Extract from DOM\nconst extracted = intelligence.extractFromDOM({\n  selector: 'table.my-table',\n  includeHeaders: true,\n  maxRows: 1000,\n  inferTypes: true,\n});\n\n// Load from Vue data grid\nintelligence.loadFromVueGrid(\n  gridData,\n  [\n    { field: 'name', header: 'Product Name' },\n    { field: 'price', header: 'Price' },\n  ],\n  { inferTypes: true }\n);\n```\n\n## 🚀 Advanced Features\n\n### 📊 Data Quality Profiling\n\nComprehensive data quality assessment and profiling:\n\n```typescript\nimport { profileData, assessDataQuality, detectDataIssues, suggestCleaningSteps } from '@aivue/tabular-intelligence';\n\n// Profile your data\nconst profile = await profileData(data, {\n  includeDistributions: true,\n  detectDataTypes: true,\n  findPatterns: true\n});\n\n// Assess data quality\nconst qualityReport = await assessDataQuality(data);\nconsole.log('Quality Score:', qualityReport.overallScore); // 0-100\n\n// Detect specific issues\nconst issues = await detectDataIssues(data);\n// Returns: missing values, outliers, duplicates, type mismatches, etc.\n\n// Get cleaning recommendations\nconst recommendations = await suggestCleaningSteps(data);\n// Returns prioritized list of cleaning actions\n```\n\n### 🧹 Smart Data Cleaning\n\nIntelligent missing value imputation and outlier handling:\n\n```typescript\nimport { imputeMissingValues, handleOutliers } from '@aivue/tabular-intelligence';\n\n// Impute missing values\nconst imputationResult = await imputeMissingValues(data, {\n  strategy: 'knn', // 'mean' | 'median' | 'mode' | 'knn' | 'iterative' | 'ai'\n  columns: ['age', 'income']\n});\n\n// Handle outliers\nconst outlierResult = await handleOutliers(data, {\n  method: 'cap', // 'remove' | 'cap' | 'transform'\n  strategy: 'iqr', // 'iqr' | 'zscore' | 'isolation_forest'\n  columns: ['price', 'quantity']\n});\n```\n\n### 🔧 Feature Engineering\n\nAutomated feature generation and selection:\n\n```typescript\nimport { autoGenerateFeatures, analyzeFeatureImportance, selectBestFeatures } from '@aivue/tabular-intelligence';\n\n// Auto-generate features\nconst engineeringResult = await autoGenerateFeatures(data, {\n  targetColumn: 'sales',\n  maxFeatures: 50,\n  includeInteractions: true,\n  includePolynomials: true,\n  includeAggregations: true\n});\n\n// Analyze feature importance\nconst importance = await analyzeFeatureImportance(data, 'sales');\n\n// Select best features\nconst selection = await selectBestFeatures(data, {\n  targetColumn: 'sales',\n  k: 10, // Select top 10 features\n  method: 'correlation'\n});\n```\n\n### ⏰ Time Series Analysis\n\nForecasting, trend detection, and seasonality analysis:\n\n```typescript\nimport { forecastTimeSeries, detectTrends, detectSeasonality, detectChangePoints } from '@aivue/tabular-intelligence';\n\n// Forecast time series\nconst forecast = await forecastTimeSeries(data, {\n  dateColumn: 'date',\n  valueColumn: 'sales',\n  horizon: 30, // Forecast 30 periods ahead\n  method: 'exponential_smoothing', // 'arima' | 'prophet' | 'exponential_smoothing' | 'lstm'\n  seasonality: 'auto',\n  confidence: 0.95\n});\n\n// Detect trends\nconst trends = await detectTrends(data, {\n  dateColumn: 'date',\n  valueColumn: 'sales'\n});\n\n// Detect seasonality\nconst seasonality = await detectSeasonality(data, {\n  dateColumn: 'date',\n  valueColumn: 'sales'\n});\n\n// Detect change points\nconst changePoints = await detectChangePoints(data, {\n  dateColumn: 'date',\n  valueColumn: 'sales',\n  sensitivity: 0.8\n});\n```\n\n### 🤖 AutoML\n\nAutomated model selection and hyperparameter tuning:\n\n```typescript\nimport { autoTrain, compareModels, tuneHyperparameters } from '@aivue/tabular-intelligence';\n\n// Auto-train best model\nconst autoMLResult = await autoTrain(data, {\n  targetColumn: 'churn',\n  taskType: 'classification', // 'classification' | 'regression'\n  metric: 'accuracy',\n  timeLimit: 300, // 5 minutes\n  models: ['linear', 'tree', 'ensemble', 'neural']\n});\n\n// Compare multiple models\nconst comparison = await compareModels(data, {\n  targetColumn: 'price',\n  taskType: 'regression',\n  models: ['linear', 'tree', 'ensemble'],\n  crossValidation: 5\n});\n\n// Tune hyperparameters\nconst tuningResult = await tuneHyperparameters(data, {\n  targetColumn: 'sales',\n  model: 'ensemble',\n  parameterGrid: {\n    n_estimators: [50, 100, 200],\n    max_depth: [5, 10, 15]\n  }\n});\n```\n\n### 🔍 Model Explainability\n\nSHAP values, feature importance, and counterfactuals:\n\n```typescript\nimport { explainPrediction, getFeatureImportance, getPartialDependence, generateCounterfactuals } from '@aivue/tabular-intelligence';\n\n// Explain a specific prediction\nconst explanation = await explainPrediction(data, {\n  rowIndex: 0,\n  targetColumn: 'churn',\n  model: 'ensemble'\n});\n\n// Get feature importance\nconst importance = await getFeatureImportance(data, 'churn');\n\n// Get partial dependence plot\nconst pdp = await getPartialDependence(data, {\n  feature: 'age',\n  targetColumn: 'churn'\n});\n\n// Generate counterfactuals\nconst counterfactuals = await generateCounterfactuals(data, {\n  rowIndex: 0,\n  desiredOutcome: 0, // Want churn = 0\n  targetColumn: 'churn',\n  maxChanges: 3\n});\n```\n\n### 📈 Statistical Testing & A/B Testing\n\nHypothesis testing and significance tests:\n\n```typescript\nimport { analyzeABTest, testSignificance, calculateSampleSize } from '@aivue/tabular-intelligence';\n\n// Analyze A/B test\nconst abTestResult = await analyzeABTest({\n  controlGroup: controlData,\n  treatmentGroup: treatmentData,\n  metric: 'conversion_rate',\n  confidenceLevel: 0.95\n});\n\n// Test statistical significance\nconst significanceTest = await testSignificance({\n  test: 'ttest', // 'ttest' | 'chi2' | 'anova' | 'mann_whitney' | 'kruskal_wallis'\n  groups: [group1, group2],\n  metric: 'revenue',\n  alpha: 0.05\n});\n\n// Calculate required sample size\nconst sampleSize = await calculateSampleSize({\n  effect: 0.2, // Effect size\n  power: 0.8,\n  alpha: 0.05\n});\n```\n\n### 📊 Visualization Recommendations\n\nSmart chart suggestions based on your data:\n\n```typescript\nimport { recommendVisualizations, generateChartSpec, detectPatterns } from '@aivue/tabular-intelligence';\n\n// Get visualization recommendations\nconst recommendations = await recommendVisualizations(data, {\n  columns: ['date', 'sales', 'category'],\n  purpose: 'exploration' // 'exploration' | 'presentation' | 'analysis'\n});\n\n// Generate chart specification\nconst chartSpec = await generateChartSpec({\n  type: 'line',\n  xColumn: 'date',\n  yColumn: 'sales',\n  groupBy: 'category',\n  data\n});\n\n// Detect patterns in charts\nconst patterns = await detectPatterns('line', data);\n```\n\n### 🔗 Multi-Table Analysis\n\nTable joins, relationship detection, and cross-table queries:\n\n```typescript\nimport { joinTables, detectRelationships, analyzeCrossTables, inferDatabaseSchema } from '@aivue/tabular-intelligence';\n\n// Join two tables\nconst joined = await joinTables({\n  leftTable: customers,\n  rightTable: orders,\n  leftKey: 'customer_id',\n  rightKey: 'customer_id',\n  joinType: 'inner' // 'inner' | 'left' | 'right' | 'outer'\n});\n\n// Detect relationships between tables\nconst relationships = await detectRelationships({\n  customers: customersData,\n  orders: ordersData,\n  products: productsData\n});\n\n// Analyze across multiple tables\nconst crossTableAnalysis = await analyzeCrossTables({\n  tables: { customers, orders, products },\n  relationships,\n  question: 'What is the total revenue by customer segment?'\n});\n\n// Infer database schema\nconst schema = await inferDatabaseSchema({\n  customers: customersData,\n  orders: ordersData\n});\n```\n\n### 📝 Auto Reporting & Insights\n\nGenerate comprehensive reports and insights:\n\n```typescript\nimport { generateReport, generateExecutiveSummary, generateInsights } from '@aivue/tabular-intelligence';\n\n// Generate comprehensive report\nconst report = await generateReport(data, {\n  format: 'markdown', // 'markdown' | 'html' | 'pdf' | 'json'\n  sections: ['summary', 'stats', 'anomalies', 'trends', 'recommendations'],\n  includeCharts: true\n});\n\n// Generate executive summary\nconst summary = await generateExecutiveSummary(data);\n\n// Generate automated insights\nconst insights = await generateInsights(data, {\n  maxInsights: 10,\n  priority: 'high'\n});\n```\n\n### 🔒 Privacy & Compliance\n\nPII detection, anonymization, and compliance checking:\n\n```typescript\nimport { detectPII, anonymizeData, checkCompliance } from '@aivue/tabular-intelligence';\n\n// Detect PII\nconst piiDetection = await detectPII(data);\nconsole.log('PII Columns:', piiDetection.piiColumns);\nconsole.log('Risk Level:', piiDetection.riskLevel);\n\n// Anonymize data\nconst anonymized = await anonymizeData(data, {\n  method: 'hashing', // 'masking' | 'hashing' | 'generalization' | 'differential_privacy' | 'tokenization'\n  columns: ['email', 'phone', 'ssn']\n});\n\n// Check compliance\nconst complianceReport = await checkCompliance(data, 'GDPR'); // 'GDPR' | 'CCPA' | 'HIPAA' | 'SOC2'\nconsole.log('Compliant:', complianceReport.compliant);\nconsole.log('Score:', complianceReport.score);\n```\n\n### 📦 Data Versioning & Pipelines\n\nSnapshots, lineage tracking, and transformation pipelines:\n\n```typescript\nimport { createSnapshot, compareSnapshots, createPipeline, executePipeline } from '@aivue/tabular-intelligence';\n\n// Create data snapshot\nconst snapshot1 = await createSnapshot(data, 'Before Cleaning');\n\n// ... perform transformations ...\n\nconst snapshot2 = await createSnapshot(cleanedData, 'After Cleaning');\n\n// Compare snapshots\nconst diff = await compareSnapshots(snapshot1.id, snapshot2.id);\n\n// Create transformation pipeline\nconst pipeline = await createPipeline([\n  { operation: 'impute_missing', params: { strategy: 'mean' } },\n  { operation: 'handle_outliers', params: { method: 'cap' } },\n  { operation: 'normalize', params: { method: 'minmax' } }\n]);\n\n// Execute pipeline\nconst result = await executePipeline(pipeline, data);\n```\n\n### 🌊 Streaming & Real-time Data\n\nReal-time data processing and monitoring:\n\n```typescript\nimport { connectStream, detectStreamingAnomalies, calculateWindowedAggregations, smartSample } from '@aivue/tabular-intelligence';\n\n// Connect to streaming source\nconst stream = await connectStream({\n  source: 'websocket', // 'websocket' | 'sse' | 'polling'\n  url: 'wss://api.example.com/stream',\n  updateInterval: 1000\n});\n\n// Detect anomalies in real-time\nconst anomalies = await detectStreamingAnomalies(streamData, {\n  columns: ['temperature', 'pressure'],\n  threshold: 3,\n  method: 'statistical'\n});\n\n// Calculate windowed aggregations\nconst aggregations = await calculateWindowedAggregations(streamData, {\n  windowType: 'tumbling', // 'tumbling' | 'sliding' | 'session'\n  windowSize: 100,\n  aggregations: [\n    { column: 'value', function: 'avg', alias: 'avg_value' },\n    { column: 'value', function: 'max', alias: 'max_value' }\n  ]\n});\n\n// Smart sampling\nconst sample = await smartSample(largeDataset, {\n  size: 1000,\n  method: 'stratified', // 'random' | 'stratified' | 'systematic' | 'cluster'\n  preserveDistribution: true\n});\n```\n\n## 📖 API Reference\n\n### `useTabularIntelligence(options)`\n\nMain composable for tabular intelligence.\n\n**Options:**\n- `config: TFMConfig` - TFM API configuration\n- `data?: Ref<any[]>` - Reactive data array\n- `schema?: Ref<TableSchema>` - Optional table schema\n- `useLocalFallback?: boolean` - Enable local analysis fallback (default: true)\n- `qaConfig?: QAEngineConfig` - Q&A engine configuration (optional)\n- `maxQuestionHistory?: number` - Maximum questions to keep in history (default: 50)\n\n**Returns:**\n- `client: TabularIntelligence` - Core TFM client instance\n- `loading: Ref<boolean>` - Loading state\n- `error: Ref<Error | null>` - Error state\n- `lastResult: Ref<AnalysisResult | null>` - Last analysis result\n- `data: Ref<any[]>` - Data array\n- `schema: Ref<TableSchema | null>` - Inferred or provided schema\n- `questionHistory: Ref<Question[]>` - Q&A question history\n- `answerHistory: Ref<Answer[]>` - Q&A answer history\n- `lastAnswer: Ref<Answer | null>` - Last Q&A answer\n- `analyze(type, options)` - Generic analysis method\n- `getDescriptiveStats()` - Get descriptive statistics\n- `detectAnomalies(columns?, sensitivity?)` - Detect anomalies\n- `performClustering(features, numClusters?)` - Perform clustering\n- `predict(targetColumn, options?)` - Make predictions\n- `askQuestion(question, options?)` - Ask a question about the data\n- `generateSummary()` - Generate AI summary of the data\n- `clearHistory()` - Clear Q&A history\n- `extractFromDOM(options?)` - Extract table from DOM\n- `loadFromVueGrid(data, columns?, options?)` - Load data from Vue grid\n- `updateConfig(config)` - Update TFM configuration\n- `initializeQA(qaConfig)` - Initialize Q&A engine\n- `setData(data, autoInferSchema?)` - Set new data\n- `reset()` - Reset state\n\n### Analysis Types\n\n```typescript\ntype AnalysisType =\n  | 'descriptive_stats'    // Mean, median, std dev, percentiles\n  | 'anomaly_detection'    // Outlier detection\n  | 'segmentation'         // Data segmentation\n  | 'clustering'           // K-means, DBSCAN, etc.\n  | 'prediction'           // Forecasting\n  | 'correlation'          // Correlation analysis\n  | 'summary'              // AI-generated summary\n  | 'qa'                   // Question answering\n  | 'trend_analysis'       // Trend detection\n  | 'outlier_detection';   // Statistical outliers\n```\n\n### TFM Configuration\n\n```typescript\ninterface TFMConfig {\n  provider: 'local' | 'tabpfn' | 'custom';\n  baseUrl: string;              // API endpoint\n  apiKey?: string;              // API key\n  model?: string;               // Model name\n  headers?: Record<string, string>; // Custom headers\n  timeout?: number;             // Request timeout (ms)\n  useCorsProxy?: boolean;       // Use CORS proxy\n  corsProxyUrl?: string;        // CORS proxy URL\n}\n```\n\n### Available TFM Providers\n\n| Provider | Description | API Required | Best For |\n|----------|-------------|--------------|----------|\n| **local** | JavaScript-based statistical analysis | ❌ No | Testing, basic stats, offline use |\n| **tabpfn** | TabPFN from Prior Labs - state-of-the-art TFM | ✅ Yes | Production, accurate predictions |\n| **custom** | Your own TFM API endpoint | ✅ Yes | Custom models, enterprise solutions |\n\n**Note:** OpenAI and Anthropic do NOT offer dedicated TFM APIs. For AI-powered insights, use the Q&A feature with OpenAI/Anthropic instead.\n\n## 🔧 Advanced Usage\n\n### TabPFN Integration\n\n```typescript\n// Get API key from https://priorlabs.ai/\nconst { analyze } = useTabularIntelligence({\n  config: {\n    provider: 'tabpfn',\n    baseUrl: 'https://api.priorlabs.ai/v1/predict',\n    apiKey: process.env.TABPFN_API_KEY,\n    timeout: 60000,\n  },\n  useLocalFallback: true, // Fallback to local if API fails\n});\n\n// Perform analysis with TabPFN\nconst result = await analyze('prediction', {\n  targetColumn: 'sales',\n  features: ['price', 'quantity', 'category'],\n});\n```\n\n### Custom TFM API Integration\n\n```typescript\nconst { analyze } = useTabularIntelligence({\n  config: {\n    provider: 'custom',\n    baseUrl: 'https://your-tfm.com/api/v1/analyze',\n    apiKey: process.env.TFM_API_KEY,\n    headers: {\n      'X-Custom-Header': 'value',\n    },\n    timeout: 60000,\n  },\n});\n\n// Custom analysis\nconst result = await analyze('clustering', {\n  features: ['age', 'income', 'spending'],\n  numClusters: 5,\n  algorithm: 'kmeans',\n});\n```\n\n### Local Analysis (No API Required)\n\n```typescript\nconst { getDescriptiveStats, detectAnomalies } = useTabularIntelligence({\n  config: {\n    provider: 'local',\n    baseUrl: '', // Not used for local\n  },\n  data: ref(myData),\n  useLocalFallback: true,\n});\n\n// These will use built-in statistical methods\nconst stats = await getDescriptiveStats();\nconst anomalies = await detectAnomalies();\n```\n\n## 🎨 Integration with @aivue/smart-datatable\n\nPerfect companion for SmartDataTable:\n\n```typescript\nimport { useSmartDataTable } from '@aivue/smart-datatable';\nimport { useTabularIntelligence } from '@aivue/tabular-intelligence';\n\nconst { data, filteredData } = useSmartDataTable({ data: ref(orders) });\n\nconst { analyze } = useTabularIntelligence({\n  config: tfmConfig,\n  data: filteredData, // Analyze filtered data\n});\n```\n\n## 📊 Example: Complete Analysis Pipeline\n\n```typescript\nconst pipeline = async () => {\n  // 1. Get descriptive statistics\n  const stats = await getDescriptiveStats();\n  console.log('Statistics:', stats);\n\n  // 2. Detect anomalies\n  const anomalies = await detectAnomalies(['price', 'quantity'], 0.8);\n  console.log('Anomalies:', anomalies);\n\n  // 3. Perform clustering\n  const clusters = await performClustering(['price', 'quantity'], 3);\n  console.log('Clusters:', clusters);\n\n  // 4. Make predictions\n  const predictions = await predict('sales', {\n    predictionHorizon: 30,\n    confidenceLevel: 0.95,\n  });\n  console.log('Predictions:', predictions);\n};\n```\n\n## 🤝 Contributing\n\nContributions are welcome! Please see [CONTRIBUTING.md](../../CONTRIBUTING.md).\n\n## 📄 License\n\nMIT © [reachbrt](https://github.com/reachbrt)\n\n","readmeFilename":"README.md"}