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Nine new functions extend the analysis and planning workflow:
check_gstudy_design() diagnoses invalid scores, missing
IDs, duplicate cells, missing crossed cells, insufficient levels and
simple nested imbalance.error_budget() shows each component’s contribution to
D-study error.sem_gtheory() reports relative and absolute standard
errors of measurement.score_interval() constructs approximate normal
measurement intervals.dstudy_grid() compares candidate facet counts in a
single table.optimize_dstudy() finds minimum-cost candidates meeting
a G or Phi target.dstudy_sensitivity() compares gains from increasing
each facet separately.simulate_gstudy() generates balanced Gaussian crossed
data.bootstrap_gstudy() estimates Gaussian parametric
percentile intervals for crossed-design variance components and D-study
quantities.New planning functions reject negative variance estimates unless the caller explicitly requests truncation, and record adjusted components. Existing D-study functions retain their original raw-estimate behavior.
Fixed numeric ID indexing in persons-by-items and nested estimators, handled unused factor levels, added validation to all G-study entry points, and rejected ambiguous facet labels. ANOVA extraction now uses the aov summary rather than requesting F-tests from a saturated model. No new package dependencies.
See inst/doc/planning-guide.md for assumptions, sources
and a worked example.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.