rdomains: Get the category of content hosted by a domain | Install and Load the package | Shalla | DMOZ | ML | VirusTotal | OpenAI GPT Models | Anthropic Claude

tubern: Access YouTube Analytics API via R | Install, Load the package | Using the package | Get Statistics of a Video

Introduction | Typical Workflow | Step 1: Prepare Your Data | Step 2: Compute Naive Learning Estimate | Step 3: Fit the LCA Model | Step 4: Extract and Interpret Results | Step 5: Get Standard Errors via Bootstrap | Step 6: Assess Model Fit | Step 7: Compare Across Groups (Optional) | The Latent Class Model | Three Latent Classes | Cell Probability Derivation | Worked Example | Identification | Implementation Verification | Parameter Recovery Demonstration | Monte Carlo Validation | Bias Assessment | Standard Error Assessment | Coverage Assessment | Visualization | Sample Size Effects | DK Model Extension | Using validate_recovery() | Individual-Level Learning Recovery | The Key Insight | Computing Posterior Class Probabilities | Comparison with Cross-Sectional IRT | Monte Carlo Comparison | Effect of Gamma (Guessing Rate) | Interpreting Posterior Probabilities | Conclusion | References | Session Info

guess: Adjust Estimates of Learning for Guessing | Measuring Learning: | Estimand | Other Issues | Standard Correction for Guessing | Latent Class Correction for Guessing | Installation | Usage | Transition Matrix | Adjusting Using the Latent Class Model | Adjust by Groups | Standard Errors | Fit | Model Criticism: Perplexity and Cross-Validation | Perplexity from aggregated data (items) | Perplexity from individual data | Cross-validation over items | Cross-validation over individuals | Model comparison | Simulation and Parameter Recovery Validation | Basic simulation (No-DK model) | Simulation with Don't Know responses | Comprehensive parameter recovery validation | Statistical Efficiency: Sample Size and Parameter Recovery | Sample Size Effect | Number of Items Effect | Parameter Scenarios | Why Individual-Level Data Provides Efficiency Gains

Installation | Load the library: | Authentication | Core Functions | File Analysis | URL Analysis | Domain Intelligence | IP Address Intelligence | Rate Limiting | Error Handling

Understanding YouTube API Quotas | Quota Costs by Operation | Tracking Quota Usage | Setting Custom Quota Limits | Authentication Strategies | API Key Authentication | OAuth2 Authentication | Which Authentication to Use? | Batch Processing | Processing Multiple Videos | Using Bulk Analysis Functions | Pagination Handling | Extracting Data from Results | Using simplify = TRUE (Default) | Using simplify = FALSE for Complex Data | Common Field Access Patterns | Error Handling & Retries | Using with_retry for Transient Errors | Handling Quota Exhaustion | Rate Limiting Best Practices | Caching for Performance | Practical Examples | Example 1: Analyze a Channel's Performance | Example 2: Trending Analysis | Example 3: Efficient Video Processing | Troubleshooting | Common Issues | Getting Help

Setup | Collecting Comments | Basic Emoji Analysis | Emoji Presence and Counts | Distribution of Emoji Usage | Emoji Frequency Analysis | Top Emojis | Temporal Analysis | Emoji Usage Over Time | Sentiment Categories | Engagement Correlation | Do emoji comments get more likes? | Cross-Video Comparison | Compare emoji usage across videos | Working with Clean Text | Performance Tips | Summary

Quick Start | Emoji Detection Functions | has_emoji() - Check for emoji presence | count_emojis() - Count emojis in text | extract_emojis() - Get emojis from text | remove_emojis() - Strip emojis from text | replace_emojis() - Substitute emojis | Common Use Cases | Filter comments with high emoji usage | Text analysis without emojis | Emoji frequency analysis | Unicode Text Processing | safe_utf8() - Ensure UTF-8 encoding | clean_youtube_text() - Clean HTML and normalize text | Troubleshooting | Emojis appear as question marks | Emoji counts seem too high | Some emojis not detected

HTTP 403 Errors | Error: "YouTube Data API has not been used in project before or it is disabled" | Error: "Access denied" or "Insufficient permissions" | Authentication Issues | Error: "Both app_id and app_secret are required" | OAuth Browser Issues | API Quota and Rate Limits | Error: "Quota exceeded" | Error: "Rate limit exceeded" (429) | Common Function Issues | get_captions() returns 403 | Comments functions return fewer results than expected | Channel lookup fails | Performance Tips | Getting Help

tuber: Access YouTube from R | Install, Load the package | Using the package | Get Statistics of a Video | Get Information About a Video | Get Caption of a Video | Search Videos | Get Comments on a video | Get statistics of all the videos in a channel

tubern: Enhanced YouTube Analytics for R | Quick Setup | New Features Overview | 1. Relative Date Ranges | 2. Helper Functions for Common Reports | Channel Overview | Top Performing Videos | Audience Demographics | Geographic Performance | Device and Platform Analysis | Daily Time Series Data | Revenue Reports (Requires Monetary Scope) | 3. Enhanced Parameter Validation | 4. Data Transformation and Analysis | Convert to Data Frames | Data Export | Quick Visualization | Data Summary | 5. Improved Error Handling and Diagnostics | Diagnostic Tools | Error Recovery | Complete Workflow Examples | Example 1: Channel Performance Dashboard | Example 2: Audience Analysis | Example 3: Content Performance Analysis | Best Practices | 1. Authentication | 2. Data Retrieval | 3. Error Handling | 4. Data Analysis | Troubleshooting | Common Issues | More Resources

Installation | Load up the lib: | Authentication | Get Information | Get Tags | Provide Feedback