# Specialist backends: when to move beyond the default stack

#### tidyILD authors

#### 2026-04-17

This article explains how tidyILD fits next to specialist R packages for multivariate dynamics, high-dimensional predictors, and full latent-variable / DSEM-style estimands. It is not a tutorial for those packages; it gives a contract, a routing table, and export patterns so you can preprocess in tidyILD and estimate elsewhere without re-deriving time rules or centering.

## Contract: what tidyILD owns vs what it does not

tidyILD is responsible for (when you use its pipeline):

- Encoding person and time, ordering within person, and gap metadata (`ild_prepare()`, `ild_meta()`).
- Within-between decomposition for interpretable mixed models (`ild_center()`, `ild_decomposition()`).
- Spacing-aware lags (`ild_lag()`, `ild_panel_lag_prepare()`, `ild_check_lags()`, `ild_crosslag()` for single-equation shortcuts).
- Provenance on analytic objects and diagnostics for models that tidyILD fits (`ild_diagnose()`, guardrails).

Specialist packages are responsible for (examples below):

- Joint multivariate time-series models, feedback systems, and time-varying parameters at the system level (e.g. dynamite, lavaan dynamic SEM, multivariate brms).
- Penalized longitudinal models and variable selection when p >> n (e.g. PGEE and related penalized GEE / regularized approaches).
- Measurement models and latent structural models beyond the conservative paths wrapped in tidyILD (ctsem, lavaan, blavaan, multivariate brms).

tidyILD does not aim to reimplement those estimators. The recommended pattern is: prepare → center → lag → export a plain data frame → fit with the specialist tool, recording package versions (e.g. `sessionInfo()` or `ild_manifest()` alongside your script).

## Decision table

Scientific situation | Often sufficient in tidyILD | Bridge (preprocess here, then …) | Primary external tools (examples)
--- | --- | --- | ---
Lag predictor → one outcome; AR1/CAR1 residuals | `ild_lag`, `ild_lme`, `ild_brms`, `ild_tvem` | — | —
Reciprocal or multivariate lags (e.g. stress ↔︎ mood), joint dynamics | Two separate `ild_lme` / `ild_crosslag` fits are not a joint likelihood | Same lags/centering; export data | dynamite; lavaan DSEM; multivariate brms; multivariate ctsem
High-dimensional time-varying predictors (p >> n), selection | `lme4` / unpenalized mixed models: unstable or non-identified | Screen or penalize outside default `ild_lme` path | PGEE; regularized GLM/GEE; strong priors in brms
Latent constructs + dynamics | Observed-variable pipeline; `ild_ctsem()` v1 is intentionally narrow | Export; specify full SEM in target package | ctsem, lavaan / blavaan DSEM
Nonlinear or non-Gaussian dynamics | `ild_tvem` (GAM); `ild_brms` with appropriate families/splines | Same | dynamite; brms; state-space with custom observation model
Time-varying coefficients (effect of X on Y evolves) | `ild_tvem`; interactions with time; random slopes | Full state-space / Bayesian TVP | dynamite; brms hierarchical structure
Causal estimands with many time-varying confounders | MSM / IPW tools in tidyILD for supported paths | High-dimensional confounding may need dedicated methods | Doubly robust / DML literature; penalized exposure/confounder models
Correlated outcomes, shared residual structure | Separate outcomes ignore cross-outcome correlation | Multivariate formula or dynamic multivariate model | dynamite; `mvbind` in brms (see `vignette("brms-dynamics-recipes", package = "tidyILD")`)

For temporal structure inside tidyILD (lags vs AR vs TVEM vs KFAS vs ctsem), start with `vignette("temporal-dynamics-model-choice", package = "tidyILD")`.

## Handoff pattern: export after prepare, center, and lag

Use [`ild_meta()`] to recover the person id column name stored in metadata (often still `"id"` in your data, but the prepared object also uses internal columns such as `.ild_id`).

    library(tidyILD)
    set.seed(1)
    d <- ild_simulate(n_id = 8, n_obs_per = 10, seed = 1)
    d$x <- rnorm(nrow(d))
    x <- ild_prepare(d, id = "id", time = "time")
    x <- ild_center(x, y, x)
    x <- ild_lag(x, dplyr::all_of(c("y", "x")), n = 1L, mode = "gap_aware")

    meta <- ild_meta(x)
    meta$ild_id
    #> [1] "id"
    # Plain data frame for any external package:
    dat <- as.data.frame(x)
    head(dat[, c(meta$ild_id, ".ild_time_num", "y", "y_wp", "y_bp", "x_lag1")])
    #>   id .ild_time_num          y        y_wp       y_bp     x_lag1
    #> 1  1             0 -0.3385631 -0.22929533 -0.1092678         NA
    #> 2  1          3600 -0.6791480 -0.56988020 -0.1092678  0.3700188
    #> 3  1          7200  0.3294368  0.43870458 -0.1092678  0.2670988
    #> 4  1         10800 -0.1315322 -0.02226439 -0.1092678 -0.5425200
    #> 5  1         14400 -0.5370741 -0.42780630 -0.1092678  1.2078678
    #> 6  1         18000 -1.2338038 -1.12453595 -0.1092678  1.1604026

Typical columns to retain for dynamic panel estimators:

- Person identifier: `meta$ild_id` (and/or `.ild_id` if present).
- Time: `.ild_time_num` and/or your original time column, depending on whether the target software expects continuous time, integer occasion, or clock time.
- Outcomes and predictors, including `_wp` / `_bp` columns if you want to preserve within-between interpretation in the exported file.
- Lag columns created by [`ild_lag()`] or [`ild_panel_lag_prepare()`].

[`ild_export_provenance()`] applies to objects that carry provenance (e.g. after modeling in tidyILD). For external fits, record inputs and script versions in your project manifest.

## Code stubs (not evaluated)

The chunks below are illustrative. They are not run during `R CMD check` because dynamite and PGEE are not required dependencies of tidyILD. Install those packages from their own instructions, read their vignettes, and adapt column names to match `dat` above.

### dynamite (multivariate dynamic models)

    # library(dynamite)
    # After building `dat` from an ILD object (see above):
    # - Map id/time/outcome columns to dynamite's expected data layout.
    # - Specify channels, lags, and priors per package documentation.
    # Example placeholder only (not valid without a real dynamite specification):
    # fit_dyn <- dynamite::dynamite(
    #   dformula = <your dformula>,
    #   data = dat,
    #   ...
    # )

### PGEE (penalized GEE / high-dimensional longitudinal)

    # library(PGEE)
    # Penalized GEE expects a long data frame with id, repeated outcome, and a matrix
    # or formula interface for high-dimensional covariates — see ?PGEE::PGEE.
    # Use `dat` from tidyILD after centering/lagging so covariates align with your estimand.
    # fit_pgee <- PGEE::PGEE(<formula or design>, data = dat, ...)

### lavaan / blavaan (DSEM)

    # library(lavaan)
    # Dynamic SEM is model-syntax-specific; export `dat` and define your model
    # in lavaan's longitudinal / DSEM extensions. tidyILD does not generate lavaan syntax.

## See also

- `vignette("temporal-dynamics-model-choice", package = "tidyILD")`
- `vignette("brms-dynamics-recipes", package = "tidyILD")` (including multivariate `mvbind` sketch)
- `vignette("ctsem-continuous-time-dynamics", package = "tidyILD")`
- `vignette("msm-identification-and-recovery", package = "tidyILD")` (causal MSM path in tidyILD)

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