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tidyILD 0.3.0

Provenance and reporting

• Provenance schema: Added schema_version = "1" and object_type to data and analysis provenance for future evolution. Step records include step_id.
• ild_report() now returns a stable schema: meta (n_obs, n_id, engine), methods, model_table, diagnostics_summary, provenance, provenance_export_path.
• ild_methods() is richer: row counts (N persons, n observations) when available, explicit IPW/weighted-estimation wording, diagnostics sentence reflects requested types (autocorrelation, Q-Q, etc.). New optional argument robust_se so methods text can mention cluster-robust SEs when used.
• Provenance vignette: Reproducible ILD workflows with tidyILD provenance (prepare, center, lag, fit, diagnostics, ild_history, ild_methods, ild_report, export, ild_compare_pipelines).
• Analysis provenance attached to ild_crosslag() and ild_ipw_refit() return values.
• Package description updated to “A reproducible, tidyverse-style framework for intensive longitudinal data analysis in R, with built-in methodological safeguards, provenance tracking, and reporting tools.”

tidyILD 0.2.0

This release builds on four pillars: methodological safeguards, modeling breadth, provenance tracking, and reporting tools.

Safeguards

• Robust uncertainty: ild_robust_se() provides cluster-robust variance estimators (CR0/CR2/CR3) via clubSandwich. tidy_ild_model() gains se = "robust" and robust_type; CIs and p-values use Wald normal approximation when using robust SEs.
• Missingness and IPW: ild_missing_model() fits missingness models (glm/glmer); ild_ipw_weights() generates inverse probability weights (stabilized or not, optional trim); ild_ipw_refit() refits ild_lme with IPW (lmer only). Documented as diagnostic/sensitivity tooling, not a full MNAR solution.
• Design and diagnostics: ild_design_check() aggregates spacing, WP/BP decomposition, and missingness with recommendations. ild_spacing() reports interval stats and AR1/CAR1 recommendation. ild_missing_bias() tests whether missingness is associated with a predictor. ild_center_plot() for WP vs BP density. WP/BP safeguard warning in ild_lme() when predictors vary at both levels (suggests ild_center()).

Modeling breadth

• Time-varying effects: ild_tvem() fits time-varying effect models using mgcv; ild_tvem_plot() visualizes the coefficient curve with confidence band.
• Power analysis: ild_power() for simulation-based power of a fixed effect (ild_simulate -> ild_lme -> effect recovery); supports lmer and nlme.
• Cross-lag and person-level: ild_crosslag() one-call pipeline (ild_lag -> ild_check_lags -> ild_lme). ild_person_model() and ild_person_distribution() for per-person fits and estimate distribution.
• Multi-stream alignment: ild_align() aligns a secondary stream to primary ILD within a time window (e.g. self-report + wearables).
• Visualization: ild_heatmap(), ild_spaghetti(), ild_circadian() (time-of-day patterns when time is POSIXct).

Provenance

• Data and analysis provenance: Preprocessing steps (ild_prepare, ild_center, ild_lag, ild_align, ild_ipw_weights) and analysis steps (ild_lme, ild_diagnostics, ild_tvem, ild_power, ild_missing_model, ild_crosslag, ild_ipw_refit) are recorded with a stable schema (version, schema_version, object_type, step_id, args, outputs).
• ild_provenance() and ild_history() return or print the recorded steps. ild_export_provenance() writes provenance to JSON or YAML for reproducibility supplements and archiving.
• ild_compare_pipelines() compares two ILD or model objects and reports differing steps and arguments.

Reporting

• ild_methods() generates a methods-style narrative from provenance (row counts, AR1/CAR1, created variables, IPW and robust SE when specified). Optional robust_se argument for cluster-robust SE mention.
• ild_report() returns a standardized list: meta (n_obs, n_id, engine), methods, model_table, diagnostics_summary, provenance, provenance_export_path. Optional provenance export in one call.
• New vignette: Reproducible ILD workflows with tidyILD provenance (ild_history, ild_methods, ild_report, export, compare).

Other improvements

• Model tidiers: augment_ild_model() and tidy_ild_model() with consistent columns across lmer/nlme; S3 print methods.
• Documentation: Package described as a reproducible, tidyverse-style framework with safeguards, provenance, and reporting. Vignettes use set.seed() / seed for determinism; optional-package examples (e.g. clubSandwich) use eval = requireNamespace(...). ild_power() examples kept small (n_sim = 25).

tidyILD 0.0.1 (initial release)

• Initial CRAN release.
• Pipeline: ild_prepare(), ild_summary(), ild_center(), ild_lag() (index, gap-aware, time-window), ild_spacing_class(), ild_missing_pattern(), ild_check_lags().
• Modeling: ild_lme() (lmer or nlme with AR1/CAR1), ild_diagnostics(), ild_plot() (trajectory, gaps, missingness, fitted, residual ACF).
• Utilities: ild_simulate(), ild_manifest() / ild_bundle() for reproducibility, broom integration for ild_lme fits.
• Data: ema_example dataset.
• Vignettes: workflow, within-between decomposition and spacing, glossary and quick-start.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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