Summarize the Current mlmr Modeling Scope
mlm_supported_models.RdReturns a table describing the modeling, reporting, import, diagnostic, and export features that are currently supported, experimental, or planned in mlmr.
Details
This helper is intended for transparent documentation and teaching. It gives users a compact way to see the current production path, advanced workflows, and areas that require extra statistical judgment or future development.
Examples
mlm_supported_models()
#> Area Status
#> 1 Model families Supported
#> 2 Nested structures Supported
#> 3 Three-level structures Supported
#> 4 Crossed random effects Experimental
#> 5 Multiple membership Planned
#> 6 Repeated measures Supported through grouping structure
#> 7 Random effects Supported
#> 8 Centering Supported
#> 9 Interactions Supported
#> 10 Categorical predictors Supported through R contrasts
#> 11 Uploaded data Supported
#> 12 Diagnostics Supported
#> 13 Reporting exports Supported
#> 14 Software citations Supported
#> Scope
#> 1 Gaussian linear mixed models are the primary production path; binomial, Poisson, negative binomial, and Gamma GLMMs are available as advanced workflows.
#> 2 Two-level nested models are supported in the app and backend.
#> 3 Three-level nested models are supported through additional grouping factors and random-effect blocks.
#> 4 Separate lme4 random-effect blocks can be specified; users should verify the design and interpretation carefully.
#> 5 Weighted membership models are outside the current lme4-backed production scope.
#> 6 Longitudinal models can be specified when occasions, persons, and higher-level units are represented by grouping variables.
#> 7 Random intercepts, random slopes, and correlated or independent random-effect structures are supported.
#> 8 No centering, grand-mean centering, and cluster-mean centering are supported for numeric predictors.
#> 9 Fixed interactions and cross-level interaction-style terms are supported.
#> 10 Factors and character variables are modeled using R's contrast system; dummy-coding summaries are reported.
#> 11 CSV, TSV/TXT, Excel, SPSS, SAS, and Stata files are supported when optional readers are installed.
#> 12 Convergence messages, singular-fit checks, gradients, overdispersion for GLMMs, and variance summaries are reported.
#> 13 APA tables, raw LaTeX, equations, Quarto-ready reports, and reproducible R code are supported.
#> 14 R/package version tables, APA software statements, and optional papaja citation code are supported.
#> User_responsibility
#> 1 Confirm distributional assumptions and link functions.
#> 2 Confirm the grouping IDs correctly represent the design.
#> 3 Confirm enough units exist at each higher level for stable estimation.
#> 4 Confirm the design is truly crossed and not a miscoded hierarchy.
#> 5 Use specialized methods outside mlmr for weighted multiple-membership models.
#> 6 Confirm time is coded appropriately and that autocorrelation assumptions are acceptable.
#> 7 Inspect convergence, singular fits, and whether the random-effects structure is supported by the data.
#> 8 Match centering choices to the research question.
#> 9 Interpret lower-order effects conditionally when interactions are included.
#> 10 Confirm reference categories and contrasts before reporting.
#> 11 Check variable types, missing data, and imported labels before fitting.
#> 12 Treat diagnostics as evidence for revision, not as automatic pass/fail decisions.
#> 13 Review all manuscript text, labels, and notation before submission.
#> 14 Confirm package citations meet the target journal or style guide.