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tidyILD 0.4.0

Specialist backends and handoff documentation

• New vignette: vignette("ild-specialist-backends", package = "tidyILD") — when to move beyond the default tidyILD stack, with a contract (what tidyILD owns vs specialist tools), a decision table (multivariate feedback, p >> n, latent/DSEM, nonlinear/TVP, HD confounding, correlated outcomes), a runnable export pattern after ild_prepare() / ild_center() / ild_lag(), and eval = FALSE code stubs for dynamite, PGEE, and lavaan (no new Suggests).
• temporal-dynamics-model-choice: feature-map rows for multivariate lag systems / feedback and high-dimensional predictors (p >> n) with a pointer to the new specialist vignette.
• brms-dynamics-recipes: Recipe 3 — multivariate outcomes with mvbind() + set_rescor(TRUE) sketch, with caveats on residual coupling vs full DSEM / dynamite.
• Navigation: _pkgdown.yml gains a “Beyond the default stack” articles section; README.md adds a Vignettes bullet; R/package.R (?tidyILD) adds a @details paragraph, a vignette list entry, and a @seealso link.

Dependency hygiene

• DESCRIPTION: Depends: R (>= 4.1.0) to match package code that uses the native pipe |> and lambda \(...) (R 4.1+). Previously R (>= 4.0.0).

tidyILD 0.3.0

Visualization layer (index, facets, predicted trajectories)

• Vignette: vignette("visualization-in-tidyILD", package = "tidyILD") maps common questions to ild_plot(), ild_autoplot(), and backend helpers; documents facet_by, ild_plot_predicted_trajectory(), and eval = FALSE templates for marginaleffects / ggeffects on x_wp / x_bp.
• ild_plot(): optional facet_by for trajectory, heatmap, gaps, and new type predicted_trajectory (observed and fitted lines vs time_var). ild_spaghetti() and ild_heatmap() pass facet_by through.
• ild_plot_predicted_trajectory(): convenience wrapper for the predicted-trajectory plot.
• pkgdown: Visualization reference group; article listed under workflows. ?tidyILD: Visualization section.

Missingness workflow (diagnostics and reporting)

• Vignette: vignette("ild-missingness-workflow", package = "tidyILD") — MAR/MNAR context, ild_missing_pattern(), compliance, cohort/hazard summaries, IPW template, limits of tidyILD.
• ild_missing_compliance(): person-level % observed, longest observed run, monotone-missing flag, optional expected_occasions.
• ild_missing_cohort(), ild_missing_hazard_first(): cohort fraction observed by .ild_seq; discrete hazard of first missing row among at-risk occasions.
• ild_missingness_report(): orchestrates compliance, pattern (with optional outcome join), cohort, hazard, optional ild_missing_model(), late-dropout flag, text snippets.
• ild_missing_pattern(): new arguments outcome, expected_occasions to enrich by_id via ild_missing_compliance().
• pkgdown / ?tidyILD: missingness functions grouped under reference; workflow article listed.

Temporal dynamics and model choice

• Vignettes: vignette("temporal-dynamics-model-choice", package = "tidyILD") (decision axes, feature map, minimal examples); vignette("brms-dynamics-recipes", package = "tidyILD") (copy-paste ild_brms templates, eval = FALSE on CRAN).
• ild_panel_lag_prepare(): build multiple lag columns with one ild_check_lags() audit and provenance step.
• ild_compare_fits(): compare a named list of fits on AIC/BIC/nobs with optional parallel guardrail_bundles for triggered-rule counts.
• ild_brms_dynamics_formula(): suggested formula list for person-varying lag slopes (identification/priors still user responsibility).
• Guardrails: GR_LAG_MEAN_STRONG_RESIDUAL_ACF_NO_AR, GR_INDEX_LAG_IRREGULAR_SPACING (contextual evaluation from ild_diagnose()).
• Benchmark harness: scenario mlm_lag_x (recovery of x_lag1 coefficient, lme4); manifest version 2; contract updated in inst/dev/BACKEND_VALIDATION_BENCHMARK_CONTRACT.md.
• pkgdown / cross-links: workflow and KFAS choosing-backend vignettes point to temporal-dynamics article.

Cross-backend validation harness (CI / developers)

• Benchmark contract: inst/dev/BACKEND_VALIDATION_BENCHMARK_CONTRACT.md — scenario matrix, tiers (smoke, nightly, full), metric and artifact schema (benchmark_raw.csv, benchmark_summary.csv, benchmark_metadata.json).
• Harness helpers: tests/testthat/helper-backend-validation-harness.R — harness_run_benchmark(), summarization, JSON threshold evaluation (optional jsonlite).
• Scripts: scripts/run-backend-validation-benchmarks.R, scripts/check-backend-validation-thresholds.R; default thresholds in inst/benchmarks/thresholds-smoke.json and thresholds-nightly.json.
• GitHub Actions: .github/workflows/backend-validation-benchmarks.yml (manual + nightly schedule; artifact upload). Does not replace R-CMD-check.
• Tests: tests/testthat/test-backend-validation-harness.R for schema and threshold logic.
• Docs: README “Backend validation benchmarks”; developer contracts in inst/dev/DEVELOPER_CONTRACTS.md; vignette vignette("benchmark-simulation-recovery") cross-link.

Random slope heterogeneity interpretation

• ild_heterogeneity(): partial-pooling summaries for lmerMod, lme, and brmsfit (from ild_brms()): long-format person-level ranef / coef-style tables with conditional SEs/CIs where available; summary with prop_gt_zero, quantiles, optional threshold + scale ("raw", "sd_x", "sd_y"); joins VarCorr SDs for lmer when names align.
• ild_tidy.ild_heterogeneity(), ild_autoplot.ild_heterogeneity() (caterpillar and histogram), print.ild_heterogeneity().
• ild_heterogeneity_stratified(): descriptive refits by subgroup with min_n_id guardrail; not a formal variance-difference test.
• Diagnostics: ild_diagnose() populates fit$heterogeneity when extraction succeeds; ild_autoplot(bundle, section = "fit", type = "heterogeneity", term = ...).
• Guardrails: GR_RE_SLOPE_VARIANCE_VERSUS_RESIDUAL_LOW, GR_PERSON_SPECIFIC_SLOPES_EMPIRICALLY_TIGHT (heuristic interpretation aids).
• Vignette: vignette("heterogeneity-interpretation", package = "tidyILD"). Developer contract: fit$heterogeneity in inst/dev/DEVELOPER_CONTRACTS.md.

MSM bootstrap inference (weighted lmer)

• ild_msm_bootstrap(): person-level cluster bootstrap for lmer fits after ild_ipw_refit() (or formula + ILD + weight column); weight_policy fixed_weights vs reestimate_weights with user weights_fn. tidy_ild_msm_bootstrap() returns ild_tidy_schema rows with interval_type = "bootstrap_percentile". Umbrella topic ?ild_msm_inference (bootstrap vs sandwich vs Bayes guidance). Provenance step ild_msm_bootstrap.

MSM balance, ESS, overlap

• ild_msm_balance(): weighted standardized mean differences (binary treatment) for named covariates; optional by-occasion stratification. ild_ipw_ess(): Kish effective sample size from a weight column (pooled or by occasion).
• ild_msm_overlap_plot(): propensity overlap densities (pooled IPTW via attr(x, "ild_iptw_fit"), or sequential MSM via attr(x, "ild_iptw_msm_fits")).
• ild_diagnose(..., balance = TRUE, balance_treatment, balance_covariates, ...) adds causal$balance (table + ESS). Guardrails GR_MSM_BALANCE_SMD_HIGH (|SMD| > 0.25) and GR_MSM_ESS_LOW (ESS vs N). ild_autoplot(bundle, section = "causal", type = "overlap", treatment = ...).

Estimand-first, history builder, and recovery harness (MSM v1)

• ild_msm_estimand() + ild_msm_fit(): estimand-first runner (v1 scope: static-regime ATE) that executes history/weighting/refit and optional robust/bootstrap inference with provenance.
• ild_msm_history_spec() + ild_build_msm_history(): declarative history construction on top of ild_lag() with deterministic lag-column manifest.
• ild_msm_simulate_scenario() + ild_msm_recovery(): causal DGP and recovery harness reporting bias, RMSE, CI coverage, and positivity stress summaries.
• New vignette: vignette("msm-identification-and-recovery", package = "tidyILD") for explicit identification assumptions and recovery workflow.

MSM remaining-gaps closeout (v1.1 hardening)

• ild_msm_fit() inference capability model: explicit inference$status (ok/degraded/unsupported), machine-readable reason, user-facing message, unified inference$summary schema (term, terms, estimate, std_error, conf_low, conf_high, interval_type, method), and strict_inference fail-fast mode instead of silent degradation.
• Bootstrap capability signaling: ild_msm_fit(..., inference = "bootstrap") now distinguishes full success vs partial/no-success replicate paths and records degraded/unsupported status codes.
• Estimand schema v1.1 (backward compatible): ild_msm_estimand() supports structured regime specs, target_time, explicit contrast metadata, and validation gates for placeholder estimand classes (att) and dynamic-rule scaffolding.
• Dynamic regime + contrasts: ild_msm_fit() evaluates deterministic dynamic rules and records explicit degraded status when full dynamic weighting is not yet implemented; ild_msm_contrast_over_time() computes time-indexed contrasts with target-time support and tidy-schema-aligned output.
• Diagnostics bridge: ild_msm_diagnose() provides one-call dispatch from ild_msm_fit outputs into the diagnostics bundle API.
• Recovery scenario grid + diagnostics: ild_msm_recovery() accepts scenario grids (positivity stress, misspecification toggles, censoring severity) and reports richer per-scenario metrics (failure/degradation rates, ESS quantiles, overlap-extreme rate, interval-calibration gap, modal inference method) with fast + extended test tiers.

IPTW / IPCW / joint MSM weights

• ild_iptw_msm_weights(): sequential MSM IPTW for time-varying binary A_t—per-occasion glm, stabilized factors, cumulative .ipw_treat within person; attributes ild_iptw_msm_fits / ild_iptw_msm_numerator_fits. Contrasts with pooled ild_iptw_weights().
• ild_iptw_weights(): logistic pooled treatment propensity weights (.ipw_treat); binary treatment only in this version.
• ild_ipcw_weights(): discrete-time IPCW for monotone dropout (.ipw_censor), pooled logistic on an internal drop_next indicator.
• ild_joint_msm_weights(): multiplies .ipw_treat × .ipw_censor into .ipw (optional mean-1 scaling and trimming) for ild_ipw_refit() / ild_diagnose().
• Diagnostics: fill_diagnostics_causal() summarizes component weights; ild_autoplot(..., section = "causal") facets joint vs. IPTW vs. IPCW when present; new guardrail GR_MSM_COMPONENT_WEIGHTS_UNSTABLE (same max/min ratio idea as joint IPW).
• ild_ipw_helpers.R: shared trim / attribute restore used by IPW and MSM weight steps.

KFAS backend (experimental)

• ild_kfas() (optional KFAS): single-subject Gaussian local level state-space models; ild_tidy(), ild_augment(), ild_tidy_states(), ild_diagnose(), ild_autoplot() methods. ild_augment() uses alphahat when muhat is absent (common under diffuse initialization), so .fitted and residuals align with the smoothed signal. ild_autoplot(ild_diagnostics_bundle) when meta$engine == "KFAS" uses KFAS-specific panels: predictive + forecast / errors (not PPC); fit + convergence (MLE text summary); residual + acf / qq / fitted. Direct ild_plot_states(), ild_plot_filtered_vs_smoothed(), ild_plot_forecast() for discoverability. ild_methods() / ild_report() describe KFAS provenance (state spec, family, smoothing, fit context, optional fitSSM dots). See inst/dev/KFAS_V1_BACKEND.md for the full v1 design and roadmap (trend, AR(1), regression+state, pooling).
• Tests: tests/testthat/helper-kfas-fixtures.R (DGP + bundle expectations); tests/testthat/test-kfas-extended.R — contract depth, local-level recovery (variances + smoothed state), missingness (documented row dropping + guardrail), extra guardrails (high irregularity, many NA segments); skipped placeholders for local_trend / ar1_state / regression_local_level recovery and GR_KFAS_STATE_DIMENSION_HIGH_FOR_N until multi-state specs ship.
• ild_diagnose() for ild_fit_kfas centers state-space residual diagnostics: standardized innovations (with rstandard / v–F fallback), ACF, normal QQ correlation, outlier flags, Ljung–Box; rich meta, data, design, fit, predictive, warnings; plus KFAS-specific guardrails (GR_KFAS_* in guardrail_registry()). Use fit_context = "independent_series_per_id" when stacking independent single-ID fits (triggers heterogeneity guardrail).

ctsem backend (first-class wrapper, v1)

• ild_ctsem(): first-class continuous-time latent-dynamics wrapper with tidyILD provenance and ILD metadata attachment.
• S3 backend parity: ild_tidy.ild_fit_ctsem(), ild_augment.ild_fit_ctsem(), ild_diagnose.ild_fit_ctsem(), and ild_autoplot.ild_fit_ctsem() integrate ctsem fits into tidy/augment/diagnostics workflows.
• Diagnostics bundle integration: ctsem fits now populate ild_diagnostics_bundle with residual/predictive summaries and section-first autoplot compatibility.
• ctsem guardrails: added GR_CTSEM_NONCONVERGENCE, GR_CTSEM_UNSTABLE_DYNAMICS, and GR_CTSEM_SHORT_SERIES_FOR_COMPLEX_DYNAMICS with deterministic trigger coverage.
• New vignette: vignette("ctsem-continuous-time-dynamics", package = "tidyILD").

Guardrails (identity & reporting)

• print() on ild_diagnostics_bundle prints a Summary after slots: counts for warnings/guardrails, highest guardrail severity, and up to five rule_id values when guardrails are present.
• ild_report() enriches diagnostics_summary with guardrails_narrative and a guardrails list (n, max_severity, rule_ids); when guardrails fired, methods_with_guardrails combines methods text with the guardrail sentence.
• ild_methods() accepts optional bundle (typically ild_diagnose(fit)) to append methodological cautions when guardrails triggered.

Documentation

• Simulation benchmark (ild_recovery_metrics(), vignette("benchmark-simulation-recovery", package = "tidyILD")): ild_recovery_metrics() summarizes bias, RMSE, and nominal Wald coverage from ild_power(..., return_sims = TRUE)$sim_results. The vignette documents the ild_simulate() DGP, fixed-effect recovery vs empirical power, sessionInfo, optional knitr chunk caching for heavy runs, and sections on variance-component snapshots, AR(1) DGP vs residual correlation, and pointers to brms / KFAS. Illustrative scope: Gaussian ild_lme() path through ild_power(), not exhaustive validation of all backends. GitHub Actions runs R CMD check on push/PR.
• CI / R CMD check portability: BH and RcppEigen in Suggests so rstan/brms tests can compile Stan models on Linux CI (Boost and Eigen headers). ild_augment.brmsfit uses stats::fitted() (S3 dispatch to brms). KFAS paths use stats::predict / stats::rstandard (instead of non-exported KFAS:: names). ASCII-only diagnostics roxygen strings; ?ild_plot_filtered_vs_smoothed usage matches the function signature.
• ild_fit() unified entry point: backend = "brms" dispatches to ild_brms() (with prior / prior_template); correlation_class other than "auto" errors for brms (nlme-only). State-space: use ild_kfas() (not via ild_fit()). For brms::brm()’s Stan backend** argument, use ild_brms() directly (name clash with ild_fit(..., backend =)).
• Tsibble interoperability (Phase 1): When ild_prepare() receives a tsibble tbl_ts (one key + index omitted, or explicit id / time while still a tbl_ts), metadata is stored in attr(x, "tidyILD")$tsibble and read via ild_tsibble_meta(); ild_spacing may include a tsibble sublist contrasting declared vs empirical intervals. ild_as_tsibble() uses stored is_regular for a best-effort round-trip. User-facing walkthrough: vignette("tsibble-interoperability", package = "tidyILD"). See also ?ild_prepare, ?ild_tsibble_meta, inst/dev/DEVELOPER_CONTRACTS.md §5.
• KFAS conceptual vignettes: vignette("kfas-state-space-modeling", package = "tidyILD") (state-space basics, filtered vs smoothed in ILD language, explicit v1 trust boundaries: discrete-time, not ctsem-like CT dynamics, not multilevel latent pooling); vignette("kfas-irregular-timing-spacing", package = "tidyILD") (spacing diagnostics first; KFAS does not “solve” irregular timing in a continuous-time sense); vignette("kfas-choosing-backend", package = "tidyILD") (decision framing: lme vs brms vs KFAS). pkgdown: _pkgdown.yml lists these three under Articles in reading order before other vignettes.
• ild_diagnose() / ild_diagnostics_bundle: sections are denser by default: data includes cohort, obs_per_id, outcome_summaries, missingness_rates; design includes flags, time_coverage, occasion_imbalance (mirrors obs_per_id); fit includes reml, optimizer, theta_summary, X_rank, residual_correlation (frequentist/brms); predictive adds n, mean_residual, max_abs_error; missingness is always structured (note, pct_na_by_var). Optional missing_model / missing_model_predictors / causal_detail gate ild_missing_model() and extra IPW quantiles.
• ild_autoplot() for ild_diagnostics_bundle is section-first: use section and type (e.g. residual + acf / qq / fitted; fit + convergence; predictive + ppc for brms; data + missingness; design + coverage; causal + weights). With section = "residual" and type = NULL, legacy bundles still return the full named list from plot_ild_diagnostics() when residual$legacy_ild_diagnostics is present. ild_diagnose() sets attr(bundle, "ild_fit") and attr(bundle, "ild_data") for plotting.
• ?ild_diagnostics_utilities: umbrella help topic describing standalone use and bundle section roles for ild_design_check(), ild_missing_pattern(), ild_missing_model(), and ild_ipw_weights() alongside ild_diagnose() / ild_diagnostics_bundle.
• Developer package standards: normative spec in inst/dev/DEVELOPER_CONTRACTS.md and vignette vignette("developer-contracts", package = "tidyILD") (diagnostics bundle sections, tidy/augment semantics, backend adapter checklist).
• Contract regression tests (tests/testthat/test-contract-regression.R, tests/testthat/helper-contract-fixtures.R): seeded scenarios assert bundle section density, expected guardrail rule_ids, ild_tidy / ild_augment columns, ild_autoplot routes, and guardrail-aware ild_methods(..., bundle) (see developer contracts).

ild_tidy() schema migration

• tidy_ild_model() and ild_tidy.brmsfit() now emit all columns from ild_tidy_schema(): conf_low / conf_high (replacing ci_low / ci_high), plus component (currently "fixed" for coefficient rows), effect_level (conservative: "population" for intercept, "within" / "between" for _wp / _bp terms, "cross_level" for interactions with both, "unknown" otherwise), statistic, interval_type ("Wald" frequentist, "quantile" brms), engine, model_class. Optional columns are included (NA for frequentist; brms fills rhat / ESS when intervals = TRUE).
• Internal helpers in ild_tidy_helpers.R; see vignette("developer-contracts", package = "tidyILD") for semantics.

ild_augment() schema migration

• augment_ild_model() and ild_augment.brmsfit() emit all ild_augment_schema() required columns: .outcome (canonical observed vector; formula-named response column removed), .resid_std (Pearson-type via residuals(..., type = "pearson") when length-matched; otherwise NA), engine, model_class, plus optional columns (mostly NA until used).
• brms: posterior interval columns are .fitted_lower / .fitted_upper (replacing .fitted_q2.5 / .fitted_q97.5).
• Helpers in ild_augment_helpers.R.

Guardrails (analysis safety layer)

• guardrail_registry(): catalog of methodological rules (rule_id, section, severity, default messages). ild_diagnose() populates ild_diagnostics_bundle$guardrails with triggered rules only, using the canonical columns above. Rules cover mixed WP/BP predictors, irregular spacing without residual correlation, high gap rates, late missingness concentration, singular fits, poor mixing / low ESS (Bayesian), and unstable IPW weights.

Contracts (Phase 1 — specifications only)

• ild_diagnostics_bundle(): canonical class for engine-agnostic diagnostics with fixed slots: meta, data, design, fit, residual, predictive, missingness, causal, warnings, guardrails, summary_text (see ?ild_diagnostics_bundle). Populated by engines in a later phase.
• ild_tidy_schema() / ild_augment_schema(): documented required and optional column names; ild_tidy() and ild_augment() outputs match these contracts in this release.

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.”

Unified API and tsibble

• ild_lme() analysis provenance records backend ("lme4" / "nlme"), fit_engine ("lmer" / "lme"), and backend_version (installed package version).
• ild_fit() wrapper to choose backend = "lme4" or "nlme" (same as ild_lme() with ar1 implied).
• ild_tidy(), ild_augment(), ild_autoplot() S3 generics dispatching to tidy_ild_model(), augment_ild_model(), and plot_ild_diagnostics() / ild_plot() as appropriate.
• ild_as_tsibble() converts an ILD object to a tbl_ts (optional tsibble in Suggests). ild_prepare() with both id and time omitted can take a tbl_ts when tsibble is installed (single key + index inferred).

Bayesian backend (brms)

• ild_brms() fits Bayesian mixed models via brms, with ild_prior_ild() templates (default, weakly_informative, minimal_shrinkage). Attributes ild_posterior (sampler settings, prior summary, divergences) and ild_provenance are attached.
• ild_tidy() / ild_augment() methods for brmsfit (posterior means, 95% intervals, R-hat, ESS; augmented fitted intervals as .fitted_lower / .fitted_upper).
• ild_diagnose() always returns ild_diagnostics_bundle for lmerMod, lme, and brmsfit (unified slots: data, design, fit, residual, predictive, missingness, causal, warnings, guardrails, summary_text). Frequentist residual plots use residual$legacy_ild_diagnostics with plot_ild_diagnostics() or ild_autoplot(bundle, section = "residual"). Call ild_diagnostics() directly if you only need the legacy object. ild_methods() and ild_report() describe priors, chains, warmup, and sampler controls for ild_brms fits.

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.