dyadMLM is an R package for preparing and checking cross-sectional and
intensive longitudinal dyadic data for linear and generalized linear
mixed-effects models. It supports the Actor–Partner Interdependence
Model, Dyad–Individual Model, and Dyadic Score Model parameterizations
and includes selected post-estimation tools. Models are fitted using
packages such as glmmTMB or brms.
Start here:
Install the stable release from CRAN:
install.packages("dyadMLM")To try the latest changes and help test upcoming features, install the development version from R-universe:
install.packages(
"dyadMLM",
repos = c(
"https://pascal-kueng.r-universe.dev",
"https://cloud.r-project.org"
)
)Development versions may change more frequently.
Questions about using dyadMLM, specifying models, or interpreting
output are welcome in Q&A
Discussions.
Early feature or method ideas can start in
Ideas,
while bugs and concrete improvements can be shared through
Issues.
Documentation, examples, tests, reviews, and code contributions are all welcome; see the contribution guide. Please only share data you are permitted to make public. Never upload identifiable or confidential research data.
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.
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
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).
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
)If you use dyadMLM, please cite the installed package version. Run:
citation("dyadMLM")
#> To cite package 'dyadMLM' in publications use:
#>
#> Küng P (2026). _dyadMLM: Tools for Dyadic Multilevel Models_.
#> University of Zurich. doi:10.5281/zenodo.22047083
#> <https://doi.org/10.5281/zenodo.22047083>. R package version
#> 0.2.0.9000, <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 0.2.0.9000},
#> url = {https://pascal-kueng.github.io/dyadMLM/},
#> doi = {10.5281/zenodo.22047083},
#> organization = {University of Zurich},
#> }Continue with the Getting Started Vignette.
Or go directly to a model-specific vignette: