Changes in version 0.2.0 (2026-04-12)                  

New features

  - eta2p() and batch_eta2p() now support design = "mixed" for models
    with both crossed and nested random effects simultaneously. A
    canonical example is participants viewing multiple photos of each
    model: photos are nested within models, but both levels are crossed
    with participants. Supply both cross_vars and nest_vars to use this
    design.

Bug fixes

  - Fixed a bug in operative effect size calculation for crossed
    designs. detect_within_between() previously used hardcoded $subj and
    $item keys to classify grouping factors as within or between, which
    caused intercept variances to be silently omitted from the operative
    denominator. Keys are now indexed by actual variable name, so the
    correct components are always included.

Breaking changes

  - batch_eta2p() output columns for within/between status are now named
    within_<varname> (e.g., within_participant, within_item) rather than
    the hardcoded within_subj and within_item. Code that references
    these columns by name will need to be updated.
  - Operative effect sizes with 3 or more crossed factors now correctly
    gate each factor's intercept variance on its within/between status.
    Previously, third and higher factors were always included in the
    operative denominator regardless of whether the effect was within or
    between those factors.

                 Changes in version 0.1.2 (2026-03-17)                  

  - Initial CRAN release.
  - eta2p() computes partial eta-squared for a single fixed effect in a
    fitted lmer model, supporting crossed and nested random effects
    structures.
  - batch_eta2p() computes partial eta-squared for all fixed effects in
    a model.
  - Crossed designs support any number of grouping factors via
    cross_vars.
  - Nested designs support automatic effect-level detection.
  - Operative effect sizes available via operative = TRUE.
  - Random slope variances are translated to the outcome scale using
    σ²_slope × σ²_X, correctly accounting for predictor scaling and
    interaction terms.