The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

tidyILD

A reproducible, tidyverse-style framework for intensive longitudinal data (ILD) analysis in R, with built-in methodological safeguards, provenance tracking, and reporting tools.
Author: Alex Litovchenko.

Install

remotes::install_github("alitovchenko/tidyILD")

Backend validation benchmarks (developers / CI)

From a source checkout of the package (directory containing DESCRIPTION), optional cross-backend simulation benchmarks can be run locally:

Rscript scripts/run-backend-validation-benchmarks.R --tier smoke --out-dir /tmp/bench
Rscript scripts/check-backend-validation-thresholds.R \
  --summary /tmp/bench/benchmark_summary.csv \
  --thresholds inst/benchmarks/thresholds-smoke.json \
  --out /tmp/bench/benchmark_checks.csv

This uses pkgload::load_all() when available. Tiers: smoke (fast), nightly, full — see inst/dev/BACKEND_VALIDATION_BENCHMARK_CONTRACT.md. GitHub Actions workflow: .github/workflows/backend-validation-benchmarks.yml (scheduled + manual dispatch; artifacts uploaded).

Quick start

library(tidyILD)

# Prepare: validate time structure, add .ild_* columns and metadata
d <- data.frame(
  id = rep(1:3, each = 5),
  time = rep(as.POSIXct(0:4 * 3600, origin = "1970-01-01"), 3),
  mood = rnorm(15)
)
x <- ild_prepare(d, id = "id", time = "time", gap_threshold = 7200)

# Inspect (summary tibble + list)
ild_summary(x)

# Within-between decomposition
x <- ild_center(x, mood)

# Spacing-aware lags (max_gap from metadata if omitted)
x <- ild_lag(x, mood, mode = "gap_aware", max_gap = 7200)

# Missingness (summary tibble + plot + by_id)
ild_missing_pattern(x, vars = "mood")

# Fit and report: tidy fixed effects, fitted vs observed, residual ACF + QQ
fit <- ild_lme(mood ~ 1 + (1 | id), data = x, ar1 = FALSE, warn_no_ar1 = FALSE)
tidy_ild_model(fit)
ild_plot(fit, type = "fitted")
ild_plot_predicted_trajectory(fit, time_var = ".ild_seq") # observed + fitted vs time
diag <- ild_diagnostics(fit); diag; plot_ild_diagnostics(diag)
bundle <- ild_diagnose(fit) # diagnostics bundle; use ild_autoplot(bundle, ...)

Pipeline

• ild_prepare() — encode longitudinal structure, spacing, gaps
• ild_summary() — one-shot summary
• ild_center() — person-mean centering (WP/BP)
• ild_lag() — index, gap-aware, or time-window lags (supports lubridate::hours(2) etc.)
• ild_decomposition() — WP/BP variance and ratio; optional WP vs BP density plot
• ild_check_lags() — lag validity (valid/invalid, pct_invalid, lag order)
• ild_panel_lag_prepare() — several ild_lag() columns + one lag audit table
• ild_crosslag() — one-call cross-lag: lag predictor, check lags, fit outcome ~ lag
• ild_compare_fits() — AIC/BIC/nobs table for a list of models (optional guardrail counts)
• ild_brms_dynamics_formula() — template brms formula for random lag slopes (does not fit)
• ild_acf() — ACF on a variable or on residuals (pre-model check for AR1)
• ild_spacing_class() — regular-ish vs irregular-ish
• ild_spacing() — spacing diagnostics (median/IQR, large gaps %, CV) and AR1/CAR1 recommendation
• ild_design_check() — aggregate spacing, WP/BP, missingness, and recommendations
• ild_center_plot() — standalone WP vs BP density plot
• ild_missing_pattern() — missingness by person/variable
• ild_missing_bias() — test if missingness is associated with a predictor (informative missingness)
• ild_align() — align secondary stream (e.g. wearables) to primary ILD within a time window
• ild_lme() — mixed-effects model (lmer or nlme with AR1/CAR1)
• ild_robust_se() — cluster-robust variance (clubSandwich); tidy_ild_model(fit, se = "robust") for robust SE/CI/p
• ild_missing_model() — model missingness from covariates; ild_ipw_weights() and ild_ipw_refit() for IPW sensitivity
• ild_iptw_weights(), ild_iptw_msm_weights(), ild_ipcw_weights(), ild_joint_msm_weights() — treatment/censoring/joint MSM weight pipelines
• ild_msm_estimand() + ild_msm_fit() — estimand-first MSM runner with explicit inference capability status and strict_inference
• ild_msm_bootstrap() / tidy_ild_msm_bootstrap() — cluster bootstrap inference for weighted lmer (fixed or re-estimated weights)
• ild_msm_balance(), ild_ipw_ess(), ild_msm_overlap_plot() — weighted balance, effective sample size, and overlap diagnostics
• ild_msm_diagnose() + ild_msm_contrast_over_time() — one-call diagnostics bridge and time-indexed post-fit contrasts
• ild_msm_simulate_scenario() + ild_msm_recovery() — causal simulation and recovery harness with scenario-grid validation
• ild_ctsem() — continuous-time latent-dynamics backend with ild_tidy(), ild_augment(), ild_diagnose(), and ild_autoplot()
• ild_tvem() — time-varying effects (GAM); ild_tvem_plot() for the coefficient curve
• Temporal model choice: vignette("temporal-dynamics-model-choice", package = "tidyILD") (lags vs AR vs TVEM vs state-space); vignette("brms-dynamics-recipes", package = "tidyILD") for Bayesian templates
• ild_person_model() — fit model per person (N-of-1); ild_person_distribution() — plot distribution of estimates
• ild_diagnostics() — residual ACF, residuals vs fitted/time (use print() for summary)
• plot_ild_diagnostics() — build diagnostic plots from an ild_diagnostics object
• ild_plot() — trajectory, heatmap, gaps, missingness, fitted vs observed, predicted trajectory (observed + fitted vs time), residual ACF; optional facet_by for panels (e.g. cluster)
• ild_plot_predicted_trajectory() — convenience wrapper for predicted trajectory vs time_var
• ild_heatmap(), ild_spaghetti() — aliases; pass facet_by through to ggplot2::facet_wrap()
• ild_diagnose() — assemble ild_diagnostics_bundle (data, design, fit, residual, …); ild_autoplot(bundle, ...) for sectioned plots
• ild_circadian() — variable by hour of day (when time is POSIXct)
• augment_ild_model() / ild_augment() — tibble per ild_augment_schema(): .outcome, .fitted, .resid, .resid_std (Pearson when available), engine, model_class, etc.
• tidy_ild_model() — fixed-effect table (estimate, SE, CI, p); use se = "robust" for cluster-robust inference
• ild_simulate() — simulated ILD (n_id, n_time/n_obs_per, ar1, wp_effect, bp_effect, irregular)
• ild_power() — simulation-based power for a fixed effect (ild_simulate → ild_lme → effect recovery)
• ema_example — built-in dataset (data(ema_example))
• broom: Load broom.mixed for tidy(fit) and augment(fit) on ild_lme fits.

Vignettes

• Specialist backends (vignette("ild-specialist-backends", package = "tidyILD")): when to move beyond lme4 / default tidyILD fits; handoffs to dynamite, PGEE, DSEM, and multivariate workflows; export patterns after ild_prepare() / ild_center() / ild_lag().
• Visualization in tidyILD: index of plots, bundle sections, facet_by, predicted trajectories, and partial-effects templates (marginaleffects / ggeffects on _wp / _bp).
• From raw data to model: full pipeline with ild_prepare() through ild_lme() and ild_plot().
• MSM identification and recovery: assumptions, estimand-first MSM workflow, strict/degraded inference behavior, and scenario-grid recovery checks.
• Continuous-time dynamics with ctsem: ild_ctsem() workflow, diagnostics, and guardrails for ctsem fits.
• Short analysis report: fit, tidy fixed effects, fitted vs observed, residual ACF and Q-Q.
• Within-between decomposition and irregular spacing: centering and gap-aware lags.
• Glossary and quick-start: function glossary and checklist.

pkgdown site

Documentation and vignettes are built with pkgdown. From the package root: pkgdown::build_site(). Config: _pkgdown.yml.

Version tags

Release tags follow vMAJOR.MINOR.PATCH and are listed in version order with:

git tag -l 'v*' --sort=version:refname

Current tags: v0.0.1, v0.2.0, v0.3.0 (each annotated message is tidyILD <version>).

License

MIT.

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.
Health stats visible at Monitor.