NEWS.md
In dyadMLM: Tools for Dyadic Multilevel Models

dyadMLM 0.3.0

prepare_dyad_data() now rejects pool names that match generated role labels in other compositions, preventing distinct groups from silently sharing an indicator column (thanks to @JorAlEs, #108).
Changed the package license from MIT to GPL-3.
Added experimental simulate_dyad_responses() and check_partner_dependence() to check response variances and partner correlations in glmmTMB models with Gaussian or selected non-Gaussian responses, including ordinal, zero-inflated, and hurdle models. Checks can be performed separately for each dyad composition. For repeated occasions, supply member and time to check_partner_dependence() to check member means and same-occasion deviations separately.
Added experimental check_dyad_residuals() for simulated probability integral transform (PIT) residuals and check_dyad_outcomes() for response distributions, SDs, extremes, and zero counts. Both use the output from simulate_dyad_responses(). The PIT residuals follow Florian Hartig's DHARMa approach and Dunn and Smyth's (1996) randomized quantile residuals, but the simulations and reference distributions account for the dyadic and temporal dependence the model includes. DHARMa is not required. A simulation study evaluates sensitivity and false-positive flags.
Added quartile_lines = "qgam" to check_dyad_residuals() to fit the PIT quartile lines with the qgam package, as DHARMa does. These lines can show trends near the ends of a numeric axis more clearly, but take much longer, and their envelopes are approximate.
Improved function help and the model-comparison example (thanks to @musanyaks, #50).
Fixed an internal vctrs error in prepare_dyad_data() when no member rows are missing and the data contain, for example, an hms column while the hms package is not loaded.
Fixed recover_exchangeable_covariance() accepting a custom shared indicator with values other than 0 and 1, such as 0.5, when difference_block = NULL. It now gives an error.
Fixed predictive means for glmmTMB 1.1.15.2 and later when the zero-inflation component has random effects but no fixed effects.
Fixed compare_nested_models() reporting a likelihood-ratio test for glmmTMB models fitted with priors. It now asks to refit them without priors.
Fixed prepare_dyad_data() silently using another column when predictors or lag1_predictors renamed a column, as in c(z = x). Renaming now gives an error.
Fixed prepare_dyad_data() failing when an APIM predictor is also the dyad, member, or time column, as in growth models that use time as a predictor.
Fixed prepare_dyad_data() failing with dplyr 1.1.0. dyadMLM now requires dplyr 1.1.1 or later.
Fixed prepare_dyad_data() keeping incomplete dyads despite incomplete_dyads = "drop" when the dyad column was named n_members. Messages and metadata also showed member counts instead of dyad IDs.
prepare_dyad_data() now gives a clear error when data is supplied twice, for example through the pipe and as an argument.
prepare_dyad_data() warns when all lag-1 values of a predictor are missing, for example when time counts years with two-year waves (#83).
Fixed recover_exchangeable_covariance() accepting member contrasts from prepare_dyad_data() when no fitted group contained both partners, for example in a model grouped by person instead of couple. It now gives an error.

dyadMLM 0.2.0

This early API-stabilization release intentionally makes a few direct, breaking changes from 0.1.0. Compatibility wrappers are not retained.
Retained generated columns now use a single leading dot instead of .dy_ for more readable model formulas. For example, .dy_x_actor becomes .x_actor. The .dy_ prefix is reserved for temporary implementation columns.
prepare_dyad_data() now uses compact composition-column names when the final data contain one composition, such as .is_female and .member_contrast_arbitrary. Set short_colnames = FALSE to retain composition-qualified names. recover_exchangeable_covariance() recognizes both forms.
prepare_dyad_data(include_arbitrary_member_contrast = TRUE) can now add arbitrary member contrasts for distinguishable compositions without changing their metadata or role indicators. This supports full and exchangeability-constrained APIMs from the same prepared data. set_exchangeable_compositions still performs reclassification for pooling and DIM preparation.
Added a summary() method for prepared data that prints the dyadic structure followed by standard summaries of all columns.
Printing prepared data now uses current dyad and composition counts. If a required column has been removed, the unavailable summary is identified instead of displaying its preparation-time count.
Generated-column tracking now records all retained composition and modeling columns. Printing and model comparison use these records rather than inferring column ownership from a prefix, and generated names are checked for collisions and valid R syntax before they are written.
prepare_dyad_data(add_apim_gmc_predictors = TRUE) now adds GMC source, actor, and partner columns alongside raw APIM columns, plus lagged variants when requested. It uses one mean over retained non-missing values, warns about skipped non-numeric predictors in mixed selections, and leaves DIM/DSM centering unchanged.
For longitudinal data, prepare_dyad_data() now temporarily adds an all-missing partner row when only one member is present at an observed dyad-occasion. This preserves partner CBP values and exact source-occasion APIM, DIM, and DSM lags. Temporary rows are removed before the prepared data are returned. This is structural completion for column construction, not imputation or the addition of analysis rows.
Numeric structural columns and numeric predictors selected for preparation now reject Inf and -Inf before model-ready columns are generated. Errors identify the affected columns and input rows; NA and NaN remain supported as missing predictor values.
Renamed compare_nested_glmmTMB_models() to compare_nested_models(). Its printed conclusion now uses a configurable alpha level and no longer recommends a model solely because the likelihood-ratio test is not significant.
recover_exchangeable_covariance() now returns an exchangeable_covariance object instead of an exchangeable_rescov object, with a clearer member-level print heading.
For brms models, recover_exchangeable_covariance() now returns posterior- mean matrices by default. Use posterior = "median" or "draws" for the corresponding alternatives.
Added package-level help at ?dyadMLM, with links to the main functions, example datasets, and getting-started documentation.
Example datasets no longer include the redundant dyad_composition column. Instead, dyads_ild now includes member-specific AR(1) residual processes.

dyadMLM 0.1.0

Initial release.
Cleaned up the pre-release public API with these direct migration mappings: group to dyad, lag_predictors to lag1_predictors, model_type to model_types, temporal_predictor_decomposition to temporal_decomposition, "time_2l" to "2l", include_compositions to keep_compositions, compare_dyad_models() to compare_nested_glmmTMB_models(), exchangeable_rescov() to recover_exchangeable_covariance(), pairs to block_pairings, pairing fields shared and difference to shared_block and difference_block, and print argument what to representation.
Renamed generated exchangeable-member contrasts from .dy_diff_{composition}_arbitrary to .dy_member_contrast_{composition}_arbitrary.
Renamed the package from interdep to dyadMLM. Package-generated columns now use the .dy_ prefix instead of .i_.
Added validation and preparation of cross-sectional and intensive longitudinal dyadic data with distinguishable, exchangeable, and mixed dyad compositions.
Added composition filtering, exchangeability overrides, and pooling.
Exchangeable-dyad difference columns now use an _arbitrary suffix.
Added temporal predictor decomposition and model-ready columns for APIM, DIM, and DSM parameterizations.
Added compare_nested_glmmTMB_models() for compatible nested glmmTMB models and recover_exchangeable_covariance() for back-transforming shared/difference random-effect covariance structures.
Added example datasets, getting-started and model-specific vignettes, and a concise print method for prepared data.