Package: Saqrlab 0.4.1

Overview | Workflow 1: Basic TNA Analysis | Step 1: Simulate Data | Step 2: Fit TNA Model | Step 3: Extract Components | Step 4: Visualize | Workflow 2: Comparing Model Types | Fit Multiple Models | Compare Models | Workflow 3: Network Comparison | Using compare_networks() | Using compare_centralities() | Using compare_edge_recovery() | Workflow 4: Batch Processing | Fitting Multiple Models | Applying Functions to Multiple Models | Comparing to Reference | Workflow 5: Grid Simulations | Create Parameter Grid | Run Grid Simulation | Analyze Results | Workflow 6: Summarizing Results | Simulation Summary | Network Summary | Data Format Conversions | Wide to Long | Long to Wide | Prepare for TNA Package | Complete Example: Recovery Study | Tips for Effective Workflows | Summary

Overview | Why Bootstrap and Power Analysis? | Bootstrap Analysis | Power Analysis | Bootstrap Simulations | Basic Bootstrap | Evaluating Bootstrap Results | Advanced Bootstrap Options | Approach 1: Grid Simulation | Running Grid Simulation | Analyzing Power Results | Finding Minimum Sample Size | Approach 2: Targeted Power Study | Interpreting Results | Key Metrics | Visualizing Power Curves | Edge Recovery Analysis | Recovery Metrics | Factors Affecting Power | Sample Size (n_sequences) | Sequence Length | Network Size (n_states) | Network Density | Comprehensive Power Study Design | Recommended Protocol | Reporting Results | Power Analysis Report Template | Summary | Key Functions | Recommended Workflow | Rules of Thumb

Overview | Network Objects | Matrices & Sequences | Hierarchical & Social Data | Simulating Network Objects | igraph Networks with simulate_igraph() | Graph Algorithms | Choosing Name Regions | Weighted Networks | statnet Networks with simulate_network() | Fitted TNA Models with simulate_tna_network() | Simulating Transition Matrices | Simple Matrices with simulate_matrix() | Matrix Types | Controlling Edge Density | Custom Node Names | Multi-Type Matrices with simulate_htna() | Custom Type Configuration | Using with tna Package | Simulating Sequences | Basic Sequences with simulate_sequences() | Providing Your Own Parameters | Selecting Learning State Categories | Adding Missing Values | Getting Parameters Back | Advanced Sequences with simulate_sequences_advanced() | Stability Modes | Simulating Hierarchical Data | Long Format with simulate_long_data() | Variable Group Sizes | Achievement Levels | One-Hot Encoded Data | Simulating with simulate_onehot_data() | Edge List Simulation | Social Networks with simulate_edge_list() | Network Properties | Complete Network Generation | Generate TNA Datasets with simulate_tna_datasets() | Generate Fitted Networks with simulate_tna_networks() | Random Probabilities with generate_probabilities() | Reproducibility | Performance Tips | Summary

Overview | Understanding Multi-Type Networks | What Makes HTNA/MLNA Different? | Educational Research Applications | Simulating Multi-Type Networks | Basic Usage | Understanding the Output | Default Learning Categories | Customizing Network Structure | Number of Types and Nodes | Custom Type Names | Custom Learning Categories | Controlling Edge Probabilities | Within vs. Between Type Connectivity | Analyzing Connectivity Patterns | Using with tna Package | Visualization with plot_htna | Visualization with plot_mlna | Fitting Models to Multi-Type Data | Advanced Applications | Group TNA Networks | Using simulate_tna_matrix() | Educational Research Examples | Example 1: Self-Regulated Learning Study | Example 2: Collaborative Learning Study | Example 3: Multimodal Data Integration | Comparing Multi-Type Networks | Comparing Two HTNA Networks | Analyzing Type-Level Patterns | Summary | Key Functions | Key Parameters | Output Components | Next Steps

Overview | Installation | Getting Started | Your First Simulation | Fitting a TNA Model | Extracting Network Components | Key Concepts | Learning States | Transition Matrices | Multi-Type Networks (HTNA/MLNA) | Basic Workflows | Workflow 1: Simulate and Analyze | Workflow 2: Compare Two Networks | Workflow 3: Bootstrap Analysis | Data Formats | Wide Format (Default) | Long Format | Next Steps | Getting Help

Overview | Available Categories | Complete Learning States Reference | Metacognitive (20 verbs) | Cognitive (30 verbs) | Behavioral (30 verbs) | Social (30 verbs) | Motivational (30 verbs) | Affective (30 verbs) | Group Regulation (9 verbs) | LMS Actions (30 verbs) | Using Learning States | Basic Retrieval | Random Sampling | Smart Selection | Biased Selection | Integration with Simulation Functions | With simulate_sequences() | Specifying Categories | With simulate_matrix() | With simulate_htna() | Direct Access to LEARNING_STATES | Global Names Dataset | Educational Research Applications | Self-Regulated Learning (SRL) | Socially Shared Regulation of Learning (SSRL) | Learning Analytics (LMS Data) | Collaborative Learning | Custom State Names | Complete Reference Table | Summary | Key Functions | Category Summary | Usage Tips