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updated docs cleanly.
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‎.github/workflows/R-CMD-check.yaml‎

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push:
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branches: [main, master]
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pull_request:
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schedule:
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- cron: "0 6 * * 1"
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workflow_dispatch:
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name: R-CMD-check
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‎.github/workflows/pkgdown.yaml‎

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types: [published]
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workflow_dispatch:
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name: pkgdown.yaml
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name: pkgdown
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permissions: read-all
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if: github.event_name != 'pull_request'
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uses: JamesIves/github-pages-deploy-action@d92aa235d04922e8f08b40ce78cc5442fcfbfa2f # v4.8.0
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with:
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clean: false
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clean: true
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branch: gh-pages
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folder: docs

‎R/interdep-generated-columns.R‎

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"apim", "predictor", "cwp", "partner", "within_person", "none", "none", 13L, ".i_*_cwp_partner", "APIM within-person partner predictor: partner's momentary deviations from their usual level",
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"apim", "predictor", "cbp", "actor", "between_person_grand_mean", "none", "none", 14L, ".i_*_cbp_actor", "APIM between-person actor predictor: actor's stable difference from the average person's usual level",
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"apim", "predictor", "cbp", "partner", "between_person_grand_mean", "none", "none", 15L, ".i_*_cbp_partner", "APIM between-person partner predictor: partner's stable difference from the average person's usual level",
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"dim", "predictor", "raw", "dyad_mean", "none", "dyad_mean", "grand_mean", 20L, ".i_*_raw_dyad_mean_gmc", "DIM dyad-mean predictor: dyad's average predictor level, grand-mean centred",
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"dim", "predictor", "raw", "dyad_mean", "none", "dyad_mean", "grand_mean", 20L, ".i_*_raw_dyad_mean_gmc", "DIM dyad-mean predictor: dyad's average predictor level, grand-mean centered",
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"dim", "predictor", "raw", "within_dyad_deviation", "none", "within_dyad_deviation", "none", 21L, ".i_*_raw_within_dyad_deviation", "DIM within-dyad predictor deviation: person's difference from the dyad average",
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"dim", "predictor", "cwp", "dyad_mean", "within_person", "dyad_mean", "none", 22L, ".i_*_cwp_dyad_mean", "DIM within-person dyad-mean predictor: shared momentary deviations in the dyad",
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"dim", "predictor", "cwp", "within_dyad_deviation", "within_person", "within_dyad_deviation", "none", 23L, ".i_*_cwp_within_dyad_deviation", "DIM within-person within-dyad predictor deviation: person's momentary deviation from the dyad average",
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"dim", "predictor", "cbp", "dyad_mean", "between_person_grand_mean", "dyad_mean", "none", 24L, ".i_*_cbp_dyad_mean", "DIM between-person dyad-mean predictor: dyad's stable usual level, grand-mean centred",
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"dim", "predictor", "cbp", "dyad_mean", "between_person_grand_mean", "dyad_mean", "none", 24L, ".i_*_cbp_dyad_mean", "DIM between-person dyad-mean predictor: dyad's stable usual level, grand-mean centered",
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"dim", "predictor", "cbp", "within_dyad_deviation", "between_person_grand_mean", "within_dyad_deviation", "none", 25L, ".i_*_cbp_within_dyad_deviation", "DIM between-person within-dyad predictor deviation: person's stable difference from the dyad's usual level",
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"undirected_dsm", "outcome", "raw", "dyad_mean", "none", "dyad_mean", "none", 30L, ".i_*_raw_dyad_mean", "DSM dyad-mean outcome: dyad's average outcome level",
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"undirected_dsm", "outcome", "raw", "within_dyad_deviation", "none", "within_dyad_deviation", "none", 31L, ".i_*_raw_within_dyad_deviation", "DSM within-dyad outcome deviation: person's difference from the dyad average"

‎README.Rmd‎

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@@ -33,15 +33,15 @@ Start with the [Getting Started](https://pascal-kueng.github.io/interdep/article
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vignette for the main data-preparation workflow. The
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[documentation site](https://pascal-kueng.github.io/interdep/) also includes
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the [function reference](https://pascal-kueng.github.io/interdep/reference/index.html).
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The package documentation is organized around the main modeling tasks:
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| Vignette | Focus |
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|---|---|
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| [Getting Started](https://pascal-kueng.github.io/interdep/articles/getting-started.html) | Data structure, validation, dyad compositions, generated columns, and basic preparation |
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| APIM | Cross-sectional APIM preparation for distinguishable, exchangeable, and mixed dyad types |
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| Intensive Longitudinal APIM | Temporal predictor decomposition and intensive longitudinal APIM preparation |
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| [Dyad-Individual Model](https://pascal-kueng.github.io/interdep/articles/Dyad-Individual-Model.html) | DIM predictor construction and APIM-DIM equivalence for exchangeable dyads |
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| Undirected Dyadic Score Model | Undirected DSM outcome and predictor construction |
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The documentation is being split around the main modeling tasks:
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| Vignette | Status | Focus |
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|---|---|---|
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| [Getting Started](https://pascal-kueng.github.io/interdep/articles/getting-started.html) | available | Data structure, validation, dyad compositions, generated columns, and basic preparation |
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| APIM | planned | Cross-sectional APIM preparation for distinguishable, exchangeable, and mixed dyad types |
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| Intensive Longitudinal APIM | planned | Temporal predictor decomposition and intensive longitudinal APIM preparation |
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| [Dyad-Individual Model](https://pascal-kueng.github.io/interdep/articles/Dyad-Individual-Model.html) | available | DIM predictor construction and APIM-DIM equivalence for exchangeable dyads |
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| Undirected Dyadic Score Model | planned | Undirected DSM outcome and predictor construction |
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## Installation

‎README.md‎

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site](https://pascal-kueng.github.io/interdep/) also includes the
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[function
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reference](https://pascal-kueng.github.io/interdep/reference/index.html).
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The package documentation is organized around the main modeling tasks:
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| Vignette | Focus |
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|----|----|
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| [Getting Started](https://pascal-kueng.github.io/interdep/articles/getting-started.html) | Data structure, validation, dyad compositions, generated columns, and basic preparation |
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| APIM | Cross-sectional APIM preparation for distinguishable, exchangeable, and mixed dyad types |
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| Intensive Longitudinal APIM | Temporal predictor decomposition and intensive longitudinal APIM preparation |
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| [Dyad-Individual Model](https://pascal-kueng.github.io/interdep/articles/Dyad-Individual-Model.html) | DIM predictor construction and APIM-DIM equivalence for exchangeable dyads |
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| Undirected Dyadic Score Model | Undirected DSM outcome and predictor construction |
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The documentation is being split around the main modeling tasks:
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| Vignette | Status | Focus |
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|----|----|----|
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| [Getting Started](https://pascal-kueng.github.io/interdep/articles/getting-started.html) | available | Data structure, validation, dyad compositions, generated columns, and basic preparation |
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| APIM | planned | Cross-sectional APIM preparation for distinguishable, exchangeable, and mixed dyad types |
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| Intensive Longitudinal APIM | planned | Temporal predictor decomposition and intensive longitudinal APIM preparation |
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| [Dyad-Individual Model](https://pascal-kueng.github.io/interdep/articles/Dyad-Individual-Model.html) | available | DIM predictor construction and APIM-DIM equivalence for exchangeable dyads |
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| Undirected Dyadic Score Model | planned | Undirected DSM outcome and predictor construction |
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## Installation
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‎dev/roadmap.md‎

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- Composition-inference debugging scratch code:
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[`debug-infer-compositions.R`](debug-infer-compositions.R)
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## Current State
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The package is currently in a public development state, not a CRAN-ready release
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state. The core data-preparation API is implemented and covered by tests, the
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README links to the pkgdown site, and GitHub Actions are configured for
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R CMD check and pkgdown publishing.
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Recently completed cleanup:
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- replaced the old combined-model wording with "mixed dyad types" or
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"mixed-composition" wording
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- renamed the mixed example datasets to:
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- `example_dyadic_crosssectional_mixed`
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- `example_dyadic_ILD_mixed`
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- `example_dyadic_ILD_mixed_tweedie`
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- kept `LICENSE` for R/CRAN's `MIT + file LICENSE` convention and
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`LICENSE.md` as the full human-readable MIT license
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- added GitHub Pages/pkgdown infrastructure and linked available vignettes from
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the README
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- kept generated `docs/` and `doc/` output ignored; pkgdown should rebuild the
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site through the GitHub Pages workflow
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Immediate next step: cleanly split and polish the vignettes. The getting-started
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vignette should become shorter and more introductory; heavier APIM, ILD APIM,
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and DSM material should move into model-specific vignettes as those pages are
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created.
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## Vignette Architecture
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- missing structural data rules
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- compact examples of `predictors`, `model_type`, `temporal_predictor_decomposition`,
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print output, and metadata
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- links to model-specific vignettes
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- links to available model-specific vignettes
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- minimal or no fitted models
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- `apim.Rmd`
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- cross-sectional APIM model construction
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- distinguishable and exchangeable APIMs
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- multiple dyad types in one APIM
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- mixed dyad types in one APIM
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- `.i_is_*`, `.i_diff_*`, and raw actor/partner predictor columns
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- `intensive-longitudinal-apim.Rmd`
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- ILD APIMs with temporal predictor decomposition
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# from the dyad average
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# .i_*_cbp_dyad_mean DIM between-person dyad-mean predictor:
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# dyad's stable usual level, grand-mean
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# centred
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# centered
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# .i_*_cbp_within_dyad_deviation DIM between-person within-dyad predictor
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# deviation: person's stable difference from
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# the dyad's usual level
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Complete these before calling the feature set CRAN-ready:
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- Rebuild and inspect generated documentation
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- run `devtools::document()`
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- render `README.Rmd`
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- build pkgdown locally when changing vignette structure or `_pkgdown.yml`
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- Review `add_dyad_individual_columns.R` carefully
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- confirm direct grouped DIM construction is final
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- Run final release checks
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- `devtools::test(reporter = "summary")`
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- `devtools::check(args = "--no-manual", error_on = "never")`
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- inspect the pkgdown site after the GitHub Pages workflow completes
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for CRAN-facing clarity
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‎tests/testthat/test-interdep_generated_columns.R‎

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description = c(
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"DIM dyad-mean predictor: dyad's average predictor level, grand-mean centered",
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"DIM within-dyad predictor deviation: person's difference from the dyad average"
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‎tests/testthat/test-print_interdep_data.R‎

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expect_added_column_description(
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".i_*_raw_dyad_mean_gmc",
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"DIM dyad-mean predictor: dyad's average predictor level, grand-mean centred"
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"DIM dyad-mean predictor: dyad's average predictor level, grand-mean centered"
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".i_*_cbp_dyad_mean",
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"DIM between-person dyad-mean predictor: dyad's stable usual level, grand-mean centred"
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"DIM between-person dyad-mean predictor: dyad's stable usual level, grand-mean centered"
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expect_true(any(grepl(".i_*_cbp_within_dyad_deviation", printed, fixed = TRUE)))
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expect_added_column_description(

‎vignettes/getting-started.Rmd‎

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### Distinguishable Gaussian APIM
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```{r fit-cross-distinguishable-gaussian, eval = has_glmmTMB}
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```{r fit-cross-distinguishable-gaussian, eval = FALSE}
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cross_exchangeable_diff_model <- glmmTMB(
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```{r fit-tweedie-distinguishable, eval = FALSE}
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```{r fit-ild-distinguishable, eval = has_glmmTMB}
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```{r fit-ild-distinguishable, eval = FALSE}
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```{r fit-ild-exchangeable, eval = has_glmmTMB}
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```{r fit-ild-exchangeable, eval = FALSE}
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```{r fit-ild-tweedie-distinguishable-latent-day-cov, eval = has_glmmTMB}
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```{r fit-ild-tweedie-distinguishable-latent-day-cov, eval = FALSE}
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covariance structures. The Tweedie dispersion stays pooled because the
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sum-diff signs identify exchangeable positions, not substantive roles.
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```{r fit-ild-tweedie-exchangeable, eval = has_glmmTMB}
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```{r fit-ild-tweedie-exchangeable, eval = FALSE}
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ild_tweedie_exchangeable_model <- glmmTMB(
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mixed_cross_targets
743743
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```{r fit-mixed-cross-sectional-gaussian, eval = has_glmmTMB}
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```{r fit-mixed-cross-sectional-gaussian, eval = FALSE}
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mixed_cross_gaussian_model <- glmmTMB(
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806806
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```{r fit-mixed-ild-gaussian, eval = has_glmmTMB}
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```{r fit-mixed-ild-gaussian, eval = FALSE}
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mixed_ild_gaussian_model <- glmmTMB(
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919919
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```{r fit-mixed-ild-tweedie, eval = has_glmmTMB}
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```{r fit-mixed-ild-tweedie, eval = FALSE}
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mixed_ild_tweedie_model <- glmmTMB(
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