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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.

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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