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tidyILD

Tidyverse-native toolkit for intensive longitudinal data (ILD).
Author: Alex Litovchenko.

Install

remotes::install_github("alitovchenko/tidyILD")

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")
diag <- ild_diagnostics(fit); diag; plot_ild_diagnostics(diag)

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_crosslag() — one-call cross-lag: lag predictor, check lags, fit outcome ~ lag
• 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_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, fitted vs observed, residual ACF
• ild_heatmap(), ild_spaghetti() — aliases for heatmap and trajectory plots
• ild_circadian() — variable by hour of day (when time is POSIXct)
• augment_ild_model() — tibble with .ild_id, .ild_time, outcome, .fitted, .resid
• tidy_ild_model() — fixed-effect table (estimate, SE, CI, p) for both engines
• 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

• From raw data to model: full pipeline with ild_prepare() through ild_lme() and ild_plot().
• 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.

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.
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