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Glossary and quick-start checklist

tidyILD authors

2026-06-12

Glossary of main functions

Function Purpose
[ild_prepare()] Turn a data frame into an ILD object: parse time, sort, add .ild_* columns and spacing metadata.
[ild_summary()] One-shot summary: n persons, n observations, time range, spacing stats, gaps.
[ild_center()] Within-between decomposition: add _bp (person mean) and _wp (within-person) columns.
[ild_lag()] Spacing-aware lags: index, gap_aware, or time_window with a resolution rule.
[ild_check_lags()] Audit lag columns: count valid vs invalid (e.g. beyond max_gap).
[ild_spacing_class()] Classify spacing as “regular-ish” or “irregular-ish” (overridable).
[ild_missing_pattern()] Tabular and (via [ild_plot()] type "missingness") heatmap of missingness.
[ild_lme()] Fit mixed-effects model: lmer (no AR) or nlme with AR1/CAR1.
[ild_diagnostics()] Residual ACF, residuals vs fitted/time, Q-Q; optional AR parameter.
[ild_plot()] Trajectory, heatmap, gaps, missingness, fitted vs observed, residual ACF.
[ild_simulate()] Simulate simple ILD for examples and tests.

Quick-start checklist

  1. Prepare: x <- ild_prepare(data, id = "id", time = "time", gap_threshold = ...)
  2. Inspect: ild_summary(x), ild_plot(x, type = "gaps"), ild_plot(x, type = "missingness")
  3. Center: x <- ild_center(x, var1, var2) (adds _bp and _wp)
  4. Lag: x <- ild_lag(x, var1, mode = "gap_aware", max_gap = ...) or mode = "time_window", window = ...
  5. Check lags (optional): ild_check_lags(x, lag_vars = c("var1_lag1"))
  6. Fit: fit <- ild_lme(formula, data = x, ar1 = TRUE) (or ar1 = FALSE)
  7. Diagnose: ild_diagnostics(fit) and ild_plot(fit, type = "fitted"), type = "residual_acf"

Wide input (strict and explicit)

ild_prepare() now supports strict wide-to-long ingestion via input_format = "wide". This conversion runs before standard ILD checks; all downstream functions still receive the same canonical long ild_tbl.

wide <- data.frame(
  id = c(1, 2),
  age = c(30, 40),
  mood_t1 = c(10, 20), mood_t2 = c(11, 21),
  stress_t1 = c(5, 6), stress_t2 = c(7, 8)
)

x <- ild_prepare(
  wide,
  id = "id",
  input_format = "wide",
  wide_keep_cols = "age",
  wide_names_pattern = "^(.+)_t(.+)$",
  wide_time_parser = "numeric"
)

Safety defaults: - non-id columns must match wide_names_pattern unless declared in wide_keep_cols; - parsed (measure, time) slots must be unique; - time tokens must parse cleanly under wide_time_parser.

Comparison with generic workflows

Task Generic R tidyILD
Time structure Manual sort, manual lags, no gap checks [ild_prepare()] + [ild_lag()] with gap or time-window rules
Within-between Manual person means, ad hoc naming [ild_center()] with consistent _bp/_wp
Residual correlation nlme syntax heavy; easy to mis-specify [ild_lme(ar1 = TRUE)] with auto or override AR1/CAR1
Lag validity Often unchecked [ild_check_lags()], gap_aware and time_window modes

Same goals, less code and fewer silent errors.

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.