Package: dyadMLM 0.3.0.9000
dyadMLM: Tools for Dyadic Multilevel Models
Provides tools for dyadic multilevel modeling with linear and generalized linear mixed-effects models. It validates and prepares long-format cross-sectional and intensive longitudinal data, including ecological momentary assessment designs, for distinguishable and exchangeable dyads. It also supports datasets containing multiple observed dyad compositions. It constructs composition-aware, model-ready variables for Actor-Partner Interdependence Models (APIMs), Dyad-Individual Models (DIMs), and Dyadic Score Models (DSMs). Prepared data can be used with model engines such as 'glmmTMB' and 'brms' for Gaussian and non-Gaussian outcomes, including counts, proportions, and skewed continuous responses. Post-estimation tools compare compatible fitted models and back-transform exchangeable sum-and-difference random-effect covariance structures into member-level quantities. Experimental predictive checks compare observed and simulated response variances and partner correlations in Gaussian and selected non-Gaussian models. Distributional diagnostics separately check response distributions and residual patterns, using simulations that retain partner and time dependence. The APIM and DSM specifications and their relationships follow Iida et al. (2018) <doi:10.1177/0265407517725407>; the multilevel sum-and-difference random-effects implementation for exchangeable dyads adapts del Rosario and West (2025) <doi:10.1177/25152459251351286>.
Authors:
dyadMLM_0.3.0.9000.tar.gz
dyadMLM_0.3.0.9000.zip(r-4.7-any)dyadMLM_0.3.0.9000.zip(r-4.6-any)dyadMLM_0.3.0.9000.zip(r-4.5-any)
dyadMLM_0.3.0.9000.tgz(r-4.6-any)dyadMLM_0.3.0.9000.tgz(r-4.5-any)
dyadMLM_0.3.0.9000.tar.gz(r-4.7-any)dyadMLM_0.3.0.9000.tar.gz(r-4.6-any)
dyadMLM_0.3.0.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
dyadMLM/json (API)
| # Install 'dyadMLM' in R: |
| install.packages('dyadMLM', repos = c('https://pascal-kueng.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/pascal-kueng/dyadmlm/issues
Pkgdown/docs site:https://pascal-kueng.github.io
- dyads_cross - Example Gaussian cross-sectional dyadic data
- dyads_ild - Example Gaussian intensive longitudinal dyadic data
- dyads_nbinom_cross - Example negative-binomial cross-sectional dyadic data
- dyads_nbinom_ild - Example negative-binomial intensive longitudinal dyadic data
dyadicdyadic-datadyadic-modeldyadic-regression-analysisglmmtmbintensive-longitudinal-dataintensive-longitudinal-studiesopensciencestats
Last updated from:fddbd2dab9. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 186 | ||
| source / vignettes | OK | 326 | ||
| linux-release | OK | 240 | ||
| macos-release | OK | 184 | ||
| macos-oldrel | OK | 190 | ||
| windows-devel | OK | 192 | ||
| windows-release | OK | 211 | ||
| windows-oldrel | OK | 236 | ||
| wasm-release | OK | 137 |
Exports:check_dyad_outcomescheck_dyad_residualscheck_partner_dependencecompare_nested_modelsprepare_dyad_datarecover_exchangeable_covariancesimulate_dyad_responses
Dependencies:clidplyrgenericsgluelifecyclemagrittrpillarpkgconfigR6rlangtibbletidyselectutf8vctrswithr
Last update: 2026-10-28
Started: 2026-07-10
Last update: 2026-10-20
Started: 2026-07-10
Last update: 2026-10-20
Started: 2026-07-12
Last update: 2026-08-25
Started: 2026-07-08
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Check outcome distributions against model simulations | check_dyad_outcomes |
| Check residual distributions and patterns | check_dyad_residuals |
| Check whether a fitted model reproduces partner dependence | check_partner_dependence |
| Compare nested glmmTMB models fitted to equivalent data | compare_nested_models |
| Example Gaussian cross-sectional dyadic data | dyads_cross |
| Example Gaussian intensive longitudinal dyadic data | dyads_ild |
| Example negative-binomial cross-sectional dyadic data | dyads_nbinom_cross |
| Example negative-binomial intensive longitudinal dyadic data | dyads_nbinom_ild |
| Prepare dyadic data for multilevel models | prepare_dyad_data |
| Recover member-level residual covariance from exchangeable random-effect blocks | recover_exchangeable_covariance |
| Simulate response datasets for predictive checks | simulate_dyad_responses |
