lme4-based tests robust to no-long-double
(noLD) builds. Under reduced numeric precision a degenerate
within/between fixture (test-contract-regression.R) could
be flagged as a singular random-effects fit, and a random-slope fit
(test-heterogeneity-lmer.R) could hit an optimizer
hard-error on a near-zero slope variance. The contract fixture now
decomposes an independent predictor so the random intercept is genuinely
identified, and the random-slope test skips gracefully if the optimizer
cannot fit the boundary model on a given platform. No user-facing
changes.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._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.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).ild_prepare(): optional
input_format = "wide" converts strict wide layouts to
canonical long ILD before existing validation (regex
wide_names_pattern with two capture groups,
wide_time_parser / wide_time_format, optional
wide_keep_cols and wide_cols); provenance
records source_was_wide and wide summary counts.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.?tidyILD: Visualization
section.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().?tidyILD: missingness
functions grouped under reference; workflow article listed.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).GR_LAG_MEAN_STRONG_RESIDUAL_ACF_NO_AR,
GR_INDEX_LAG_IRREGULAR_SPACING (contextual evaluation from
ild_diagnose()).mlm_lag_x
(recovery of x_lag1 coefficient, lme4); manifest version 2;
contract updated in
inst/dev/BACKEND_VALIDATION_BENCHMARK_CONTRACT.md.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).tests/testthat/helper-backend-validation-harness.R —
harness_run_benchmark(), summarization, JSON threshold
evaluation (optional jsonlite).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/workflows/backend-validation-benchmarks.yml (manual
+ nightly schedule; artifact upload). Does not replace
R-CMD-check.tests/testthat/test-backend-validation-harness.R for schema
and threshold logic.inst/dev/DEVELOPER_CONTRACTS.md;
vignette vignette("benchmark-simulation-recovery")
cross-link.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.ild_diagnose() populates
fit$heterogeneity when extraction
succeeds;
ild_autoplot(bundle, section = "fit", type = "heterogeneity", term = ...).GR_RE_SLOPE_VARIANCE_VERSUS_RESIDUAL_LOW,
GR_PERSON_SPECIFIC_SLOPES_EMPIRICALLY_TIGHT
(heuristic interpretation aids).vignette("heterogeneity-interpretation", package = "tidyILD").
Developer contract: fit$heterogeneity in
inst/dev/DEVELOPER_CONTRACTS.md.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.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$balance (table + ESS).
Guardrails GR_MSM_BALANCE_SMD_HIGH (|SMD|
> 0.25) and GR_MSM_ESS_LOW (ESS vs
N).
ild_autoplot(bundle, section = "causal", type = "overlap", treatment = ...).ild_msm_estimand() +
ild_msm_fit(): estimand-first runner (v1
scope: static-regime ATE) that executes history/weighting/refit and
optional robust/bootstrap inference with provenance.ild_msm_history_spec() +
ild_build_msm_history(): declarative
history construction on top of ild_lag() with deterministic
lag-column manifest.ild_msm_simulate_scenario() +
ild_msm_recovery(): causal DGP and
recovery harness reporting bias, RMSE, CI coverage, and positivity
stress summaries.vignette("msm-identification-and-recovery", package = "tidyILD")
for explicit identification assumptions and recovery workflow.ild_msm_fit() inference capability
model: explicit inference$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.ild_msm_fit(..., inference = "bootstrap") now distinguishes
full success vs partial/no-success replicate paths and records
degraded/unsupported status codes.ild_msm_estimand() supports structured regime specs,
target_time, explicit contrast metadata, and validation
gates for placeholder estimand classes (att) and
dynamic-rule scaffolding.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.ild_msm_diagnose()
provides one-call dispatch from ild_msm_fit outputs into
the diagnostics bundle API.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.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().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.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/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).ild_ctsem(): first-class
continuous-time latent-dynamics wrapper with tidyILD provenance and ILD
metadata attachment.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.ild_diagnostics_bundle with residual/predictive
summaries and section-first autoplot compatibility.GR_CTSEM_NONCONVERGENCE,
GR_CTSEM_UNSTABLE_DYNAMICS, and
GR_CTSEM_SHORT_SERIES_FOR_COMPLEX_DYNAMICS with
deterministic trigger coverage.vignette("ctsem-continuous-time-dynamics", package = "tidyILD").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.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)$sim_results.
The vignette documents the ild_simulate()
DGP, fixed-effect recovery vs empirical power,
sessionInfo, optional
knitr chunk caching for heavy runs, and sections on
variance-component snapshots, AR(1) DGP vs residual correlation, and
pointers to brms / KFAS. Illustrative
scope: Gaussian ild_lme() path through
ild_power(), not exhaustive validation of
all backends. GitHub Actions runs
R CMD check on push/PR.R CMD check portability:
BH and
RcppEigen in
Suggests so
rstan/brms tests can compile Stan
models on Linux CI (Boost and Eigen headers).
ild_augment.brmsfit uses
stats::fitted() (S3 dispatch to
brms). KFAS paths use
stats::predict /
stats::rstandard (instead of non-exported
KFAS:: names). ASCII-only diagnostics
roxygen strings;
?ild_plot_filtered_vs_smoothed usage
matches the function signature.ild_fit() unified entry point:
backend = "brms" dispatches to
ild_brms() (with
prior /
prior_template);
correlation_class other than
"auto" errors for brms (nlme-only).
State-space: use ild_kfas() (not via
ild_fit()). For
brms::brm()’s Stan backend**
argument, use ild_brms() directly (name
clash with ild_fit(..., backend =)).ild_prepare() receives a
tsibble
tbl_ts (one key + index omitted, or
explicit id /
time while still a
tbl_ts), metadata is stored in
attr(x, "tidyILD")$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.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.inst/dev/DEVELOPER_CONTRACTS.md and
vignette
vignette("developer-contracts", package = "tidyILD")
(diagnostics bundle sections, tidy/augment semantics, backend adapter
checklist).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
migrationtidy_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).ild_tidy_helpers.R; see
vignette("developer-contracts", package = "tidyILD")
for semantics.ild_augment() schema
migrationaugment_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)..fitted_lower /
.fitted_upper (replacing
.fitted_q2.5 /
.fitted_q97.5).ild_augment_helpers.R.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.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.schema_version = "1" and object_type to data
and analysis provenance for future evolution. Step records include
step_id.meta (n_obs, n_id, engine), methods,
model_table, diagnostics_summary,
provenance, provenance_export_path.robust_se so methods text can
mention cluster-robust SEs when used.ild_crosslag() and ild_ipw_refit() return
values.backend ("lme4" / "nlme"),
fit_engine ("lmer" / "lme"), and
backend_version (installed package version).backend = "lme4" or "nlme" (same as
ild_lme() with ar1 implied).tidy_ild_model(), augment_ild_model(), and
plot_ild_diagnostics() / ild_plot() as
appropriate.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).default, weakly_informative,
minimal_shrinkage). Attributes
ild_posterior (sampler settings, prior summary,
divergences) and ild_provenance are attached.brmsfit (posterior means, 95% intervals, R-hat, ESS;
augmented fitted intervals as .fitted_lower /
.fitted_upper).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.This release builds on four pillars: methodological safeguards, modeling breadth, provenance tracking, and reporting tools.
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.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.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()).ild_tvem() fits
time-varying effect models using mgcv; ild_tvem_plot()
visualizes the coefficient curve with confidence band.ild_power() for
simulation-based power of a fixed effect (ild_simulate -> ild_lme
-> effect recovery); supports lmer and nlme.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.ild_align()
aligns a secondary stream to primary ILD within a time window
(e.g. self-report + wearables).ild_heatmap(),
ild_spaghetti(), ild_circadian() (time-of-day
patterns when time is POSIXct).robust_se argument for
cluster-robust SE mention.meta (n_obs, n_id, engine), methods,
model_table, diagnostics_summary,
provenance, provenance_export_path. Optional
provenance export in one call.augment_ild_model() and
tidy_ild_model() with consistent columns across lmer/nlme;
S3 print methods.set.seed() / seed for
determinism; optional-package examples (e.g. clubSandwich) use
eval = requireNamespace(...). ild_power()
examples kept small (n_sim = 25).ild_prepare(), ild_summary(),
ild_center(), ild_lag() (index, gap-aware,
time-window), ild_spacing_class(),
ild_missing_pattern(), ild_check_lags().ild_lme() (lmer or nlme with AR1/CAR1),
ild_diagnostics(), ild_plot() (trajectory,
gaps, missingness, fitted, residual ACF).ild_simulate(), ild_manifest()
/ ild_bundle() for reproducibility, broom integration for
ild_lme fits.ema_example dataset.