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.
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..dy_diff_{composition}_arbitrary to
.dy_member_contrast_{composition}_arbitrary.interdep to
dyadMLM. Package-generated columns now use the
.dy_ prefix instead of .i_._arbitrary suffix.compare_nested_glmmTMB_models() for compatible
nested glmmTMB models and
recover_exchangeable_covariance() for back-transforming
shared/difference random-effect covariance structures.