tidyILD

Tidyverse-native toolkit for intensive longitudinal data (ILD).

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
ild_summary(x)

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

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

Pipeline

• ild_prepare() — encode longitudinal structure, spacing, gaps
• ild_summary() — one-shot summary
• ild_center() — person-mean centering (WP/BP)
• ild_lag() — index or gap-aware lags
• ild_spacing_class() — regular-ish vs irregular-ish
• ild_missing_pattern() — missingness by person/variable
• ild_lme() — mixed-effects model (lmer or nlme with AR1/CAR1)
• ild_diagnostics() — residual ACF, residuals vs fitted/time
• ild_plot() — trajectory, heatmap, gaps, fitted vs observed, residual ACF
• ild_simulate() — simple simulated ILD for examples
• ild_check_lags() — audit lag columns (valid vs invalid)
• 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().
• Within-between decomposition and irregular spacing: centering and gap-aware lags.
• Glossary and quick-start: function glossary and checklist.

License

MIT.