Changelog

Source: NEWS.md

Saqrlab 0.4.0

Saqrlab is now simulation-only: network estimation code (build_network, bootstrap_network, permutation_test, temporal_network, mlvar, GIMME, MCML, higher-order networks, etc.) has moved to the sibling package Nestimate. This release focuses the package on synthetic-data generation with known ground truth.

New Features

Unified saqr_sim interface

• New saqr_sim S3 class wraps every numerical simulator’s output with a consistent $data / $params / $type / $seed interface, plus [, as.data.frame(), head(), tail(), dim(), names(), str(), print(), and summary() methods. $data / $params access is backward-compatible.
• simulate() — single dispatcher that routes a type string to the matching explicit-parameter simulator.
• list_simulators() — catalogs the available simulation types as a data.frame.
• validate_recovery() — compares a method’s point estimates to the known true params, returning a tidy per-parameter recovery table (absolute/relative error and a within-tolerance flag).

Explicit-parameter simulators

• simulate_ttest(), simulate_anova(), simulate_correlation(), simulate_clusters(), simulate_prediction() — generate data from user-specified means, SDs, correlation/covariance matrices, cluster centers, and regression coefficients; ground-truth effect sizes returned in params.
• simulate_longitudinal() — VAR(1) multilevel panel data for mlVAR/ESM with explicit temporal, contemporaneous, and between-person matrices.

New simulation modules

• inject_missingness() — adds MCAR, MAR, or MNAR missingness to any simulated data frame at a controlled rate.
• simulate_mlm() and simulate_growth() — multilevel and latent-growth / longitudinal data from explicit fixed effects and variance components.
• simulate_irt() — item response theory data (1PL/2PL/3PL) from explicit item parameters and latent abilities.
• simulate_survival() — time-to-event data with explicit hazard, covariate effects, and censoring.
• simulate_hmm() — hidden Markov model sequences from explicit emission and transition probabilities.

Scenario presets

• list_scenarios(), get_scenario(), and run_scenario() — named simulation recipes for common designs.
• tidy_simulation_results() — flattens simulation results to a data.frame.
• export_simulation() — writes results to disk.

Testing

• Added a cross-package fixture-contract test that guards the simulation output contract relied on by downstream packages.

Bug Fixes

• Fixed simulate_group_tna_networks() (group TNA network generation).
• Fixed compare_networks() / network-comparison summaries.
• Fixed grid-simulation result summaries.

Saqrlab 0.1.0

Initial release of Saqrlab - Simulation and Analysis Tools for Temporal Network Analysis.

New Features

Data Simulation

• simulate_matrix() - Generate simple transition matrices with learning state names
• simulate_htna(), simulate_mlna(), simulate_mtna() - Multi-type matrices for hierarchical/multilevel network analysis
• simulate_sequences() - Generate Markov chain sequences with optional learning states
• simulate_sequences_advanced() - Sequences with stability modes for realistic patterns
• simulate_long_data() - Hierarchical group data with actors, groups, and courses
• simulate_onehot_data() - One-hot encoded format for sequence data
• simulate_edge_list() - Social network edge lists simulation

Network Generation

• simulate_tna_datasets() - Complete TNA datasets with sequences and parameters
• simulate_tna_networks() - Fitted TNA models with random parameters
• simulate_group_tna_networks() - Group TNA models for multi-group analysis
• simulate_tna_matrix() - HTNA/MLNA matrices with node types
• generate_probabilities() - Random transition matrices and initial probabilities

Model Fitting

• fit_network_model() - Fit TNA, fTNA, cTNA, and aTNA models

Network Comparison

• compare_networks() - Compare two networks using correlation, RMSE, MAE, and more
• compare_centralities() - Compare centrality profiles between networks
• compare_edge_recovery() - Edge recovery metrics (precision, recall, F1)

Batch Processing

• batch_fit_models() - Fit models to multiple datasets in parallel
• batch_apply() - Apply any function to a list of objects

Bootstrap & Simulation Studies

• run_bootstrap_simulation() - Bootstrap analysis for stability testing
• run_grid_simulation() - Parameter grid search simulations
• run_network_simulation() - Comprehensive model comparison studies
• run_bootstrap_iteration() - Evaluate single bootstrap runs
• summarize_grid_results() - Analyze grid simulation output

Learning States

• get_learning_states() - Get learning verbs by category
• list_learning_categories() - Show available categories with counts
• select_states() - Intelligent state selection based on network size
• LEARNING_STATES - Full dataset of 180+ learning verbs in 8 categories
• GLOBAL_NAMES - 300 diverse names for simulation
• get_global_names() - Retrieve names from the global names dataset

Utilities

• generate_param_grid() - Create parameter combination grids
• validate_sim_params() - Validate and set defaults for simulation parameters
• summarize_simulation() - Summary statistics for simulation results
• summarize_networks() - Network-level summaries for model lists

Improvements

• Standardized parameter names across all functions
• use_learning_states = TRUE as default for realistic educational simulations
• Parallel processing support via future and future.apply
• Progress reporting with progressr

Dependencies

• Core: tna, seqHMM
• Data manipulation: dplyr, tidyr
• Parallel processing: parallel, future, future.apply
• Utilities: progressr, lhs