dyadMLM

dyadMLM provides tools for dyadic multilevel modeling with linear and generalized linear mixed-effects models.

It provides supporting functions for:

1. Data preparation and validation of dyadic data
2. Post-estimation tools

You can install the development version with:

install.packages("dyadMLM", repos = c(
  "https://pascal-kueng.r-universe.dev",
  "https://cloud.r-project.org"
  )
)

1. Data preparation and validation

The core feature of this package is data preparation and validation for various types of dyadic data. It creates model-ready columns for dyadic multilevel models, including the Actor-Partner Interdependence Model (APIM), Dyad-Individual Model (DIM), and the Dyadic Score Model (DSM).

The package currently supports:

• cross-sectional and intensive longitudinal dyadic data (e.g., daily diary data)
• distinguishable and exchangeable (indistinguishable) dyads
• datasets containing multiple dyad compositions (e.g., opposite-sex partners and same-sex partners)

See the Getting Started vignette.

2. Post-estimation tools

Selected post-estimation tools currently include:

• a function to compare compatible nested models
• a function to back-transform exchangeable random-effect covariance structures into interpretable member-level quantities, as described in the APIM vignette

Vignettes and examples

Start with the vignettes, or scroll down for a quick example.

Vignette	Focus
Getting Started	Data structure, validation, dyad compositions, generated columns, and basic preparation
Actor-Partner Interdependence Model	APIM preparation and formulas for distinguishable and exchangeable dyads in cross-sectional and intensive longitudinal data
Dyad-Individual Model	DIM predictor construction, formulas, and an interactive demonstration of APIM-DIM equivalence for exchangeable dyads
Dyadic Score Model	DSM predictor-score and contrast construction, formulas, and the relationship between the DSM and APIM for distinguishable dyads

For theoretical foundations and a practical walkthrough of dyadic data analysis, from data preparation and model fitting to interpretation and diagnostics using dyadMLM with glmmTMB, see the Dyadic Data Analysis Workshop. For a Bayesian workflow using dyadMLM and brms, refer to Distinguishable and Exchangeable Dyads: Bayesian Multilevel Modelling (source, DOI).

Simple Cross-Sectional Example

Prepare distinguishable dyads for a cross-sectional APIM:

library(dyadMLM)

prepared_data <- prepare_dyad_data(
  dyads_cross,
  dyad = coupleID,
  member = personID,
  role = gender,
  predictors = provided_support,
  model_types = "apim",
  # All three observed compositions in `dyads_cross` are detected and retained by
  # default. This example focuses on `female-male` dyads, so we restrict the
  # analysis here.
  keep_compositions = "female-male"
)

print(prepared_data, n = 4)
#> # dyadMLM data
#> # Rows: 240 | Dyads: 120 | Intensive longitudinal: no
#> # Structure: dyad = coupleID, member = personID, role = gender
#> #
#> # Dyad compositions:
#> # female_x_male distinguishable 120 dyads
#> #
#> # Added columns:
#> #   .composition       inferred dyad composition
#> #   .composition_role  composition-specific member role
#> #   .is_{role}         composition-role indicator columns
#> #   .{pred}_actor      APIM actor predictor: actor's original predictor values
#> #   .{pred}_partner    APIM partner predictor: partner's original predictor
#> #                      values
#> #
#> # A tibble: 240 × 11
#>   personID coupleID gender closeness provided_support .composition
#>      <int>    <int> <fct>      <dbl>            <dbl> <fct>
#> 1        1        1 female      4.71             4.49 female_x_male
#> 2        2        1 male        4.61             4.76 female_x_male
#> 3        3        2 female      6.69             4.09 female_x_male
#> 4        4        2 male        5.98             6.20 female_x_male
#> # ℹ 236 more rows
#> # ℹ 5 more variables: .composition_role <fct>, .is_female <dbl>,
#> #   .is_male <dbl>, .provided_support_actor <dbl>,
#> #   .provided_support_partner <dbl>

The prepared data contains the composition indicators and APIM actor/partner predictor columns used in the model formulas below.

One simple distinguishable APIM formula is:

simple_apim <- glmmTMB::glmmTMB(
  closeness ~

    # Gender-specific intercepts
    0 + .is_female + .is_male +

    # Gender-specific actor effects
    .provided_support_actor:.is_female +
    .provided_support_actor:.is_male +

    # Gender-specific partner effects
    .provided_support_partner:.is_female +
    .provided_support_partner:.is_male +

    # Dyad-level random effects represent the two members'
    # residual covariance structure
    us(0 + .is_female + .is_male | coupleID),

  # With the residual covariance represented by the dyad-level
  # random effects above, the Gaussian residual dispersion is fixed near zero.
  dispformula = ~ 0,
  family = gaussian(),
  data = prepared_data
)

Citation

If you use dyadMLM, please cite the version of the package you use. Obtain the citation via:

citation("dyadMLM")
#> To cite package 'dyadMLM' in publications use:
#>
#>   Küng P (2026). _dyadMLM: Tools for Dyadic Multilevel Models_.
#>   doi:10.5281/zenodo.21481720
#>   <https://doi.org/10.5281/zenodo.21481720>. R package version
#>   0.1.0.9000, <https://pascal-kueng.github.io/dyadMLM/>.
#>
#> A BibTeX entry for LaTeX users is
#>
#>   @Manual{,
#>     title = {dyadMLM: Tools for Dyadic Multilevel Models},
#>     author = {Pascal Küng},
#>     year = {2026},
#>     note = {R package version 0.1.0.9000},
#>     url = {https://pascal-kueng.github.io/dyadMLM/},
#>     doi = {10.5281/zenodo.21481720},
#>   }

---

Continue with the Getting Started Vignette.

Or go directly to a model-specific vignette:

• Actor-Partner Interdependence Model (APIM) vignette,
• Dyad-Individual Model vignette, or
• Dyadic Score Model vignette.