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Changelog

Source: NEWS.md

anovapowersim (development version)

  • Added sim_correction = c("auto", "GG", "none") to all simulation power functions. The default "auto" preserves existing behavior, while users can now prespecify corrected or uncorrected simulated tests. Uncorrected tests under nonsphericity warn that excess rejection reflects alpha inflation.
  • Fixed power_n() and power_n_calc() treating n_start as an implicit lower bound when power at that value already met the target. Both searches now probe the smallest valid sample size and refine the resulting lower bracket before reporting n_needed.
  • Balanced simulation functions now issue a once-per-session message when a custom means_pattern is resolved, clarifying that its values are projected, normalized, and rescaled to target_pes, unlike the literal means supplied through cell_design(). Both documentation pages now cross-reference this semantic distinction.
  • cell_design() now messages the count and exact factor-level combinations of cells created by default_n and default_m, making accidental factor levels visible instead of silently expanding the design.
  • power_unbalanced() now warns when the deterministic reference data imply essentially zero partial eta squared for the tested term, pointing users to possible mean typos or a mismatched term.
  • Unbalanced within-subject designs now reject : in level values and reject duplicate cell names produced by joining multi-factor levels with _, with errors that identify the problematic levels or colliding cells before any correlations are assigned.
  • Unbalanced power print and summary output now explain that simulated sample partial eta squared is upward-biased and that its mean, median, and interval are diagnostics rather than population/reference effects.
  • power_unbalanced() now warns when ss_type = "I" is used with unequal sample sizes, explains that sequential sums of squares are order-dependent, and reports the factor order inherited from cell_design().
  • Balanced simulation power functions now require custom covariance inputs to be created by within_covariance() and reject raw matrices, eliminating ambiguous assumptions about within-cell row and column order.
  • Added means_pattern() and an optional means_pattern argument to power_curve(), power_n(), power_achieved(), power_sensitivity(), and design_term_means(). Sparse relative cell values accept one-based indices or exact generated balanced-design level names, are broadcast over omitted factors, and are projected onto the requested ANOVA term before uniform calibration to target_pes.
  • Balanced simulations now use a normalized centered-linear/Kronecker direction when no explicit pattern is supplied. Results record and print whether this documented default or a custom pattern was used.
  • Balanced simulations now warn when an implicit default direction is consequential: the tested within-subject component has more than one degree of freedom and its population Greenhouse–Geisser epsilon is below one. Under nonsphericity, power_sim can depend on mean direction even when target_pes and covariance are fixed. Calculation-only functions retain the conventional direction-insensitive noncentral-F approximation and do not warn; power_unbalanced() already receives literal means.
  • The gpower = TRUE warning is now issued whenever gpower = TRUE is used, not only for within-subject terms with more than one degree of freedom. G*Power’s estimates can differ from target_pes more broadly than that; the default gpower = FALSE remains recommended.
  • power_n() now rejects n_start values above n_max instead of running the first simulation outside the requested search range.
  • Balanced simulation results now retain the full-precision simulated power used by adaptive searches and report valid/failed fit counts. Printed power values remain formatted to three decimals.
  • Breaking: simulation APIs now require one common marginal variance. within_covariance() replaces default_sd with sd and removes measurement-specific standard_deviations; direct covariance matrices must have equal diagonal variances. For unbalanced designs, remove cell-level sd and default_sd from cell_design() and supply the common SD through unbalanced_covariance(sd = ...). Unequal correlations and Greenhouse–Geisser correction remain supported.
  • Simulation functions now warn when an omitted covariance causes the common sd = 1 or within-subject correlation 0.5 defaults to be used. The covariance constructors warn when sd is omitted, and resolved covariance specifications warn when default_correlation fills unnamed pairs while preserving every explicitly supplied correlation.
  • Added the experimental, development-version-only cell_design(), unbalanced_covariance(), and power_unbalanced() functions for simulation-only power analysis of a fixed unbalanced allocation with user-defined cell means and sample sizes under a common standard deviation and optional within-subject correlations. Results include simulated power and partial eta-squared diagnostics, but deliberately omit calculated power.
  • power_unbalanced() derives the population Greenhouse–Geisser epsilon from the covariance matrix shared across between-subject cells, reports it as $epsilon, and bases power_sim on the Greenhouse–Geisser-corrected simulated p-value whenever that epsilon is below 1 (requires ss_type "III" or "II"; a warning is issued if ss_type = "I" is combined with a non-spherical design).
  • Breaking (experimental): cell_design() now takes a within argument (character vector of within-subject factor names, or NULL) and stores it on the returned design; power_unbalanced() no longer accepts within and reads it from the design instead. Move within = ... from power_unbalanced() into cell_design().
  • cell_design() gained default_n and default_m. Supply both to auto-fill any missing cells in the complete factorial design; supplying only one is an error, and supplying neither requires every cell to be defined explicitly (as before).
  • cell_design() now reports the exact missing factor-level combinations when a design is incomplete, instead of only a count, and errors clearly when a factor has fewer than two observed levels (previously this only surfaced later, inside power_unbalanced(), with an unhelpful low-level contrast-fitting error).
  • The within-subject n-consistency check (that n is identical across all within-subject rows of the same between-subject cell) now runs in cell_design() at construction time; it previously only surfaced inside power_unbalanced().
  • Added the experimental, development-version-only power_achieved() function for simulation-based achieved-power estimation at a fixed sample size and partial eta squared.
  • Added the experimental, development-version-only power_sensitivity() function for simulation-based minimum-detectable partial eta-squared searches at a fixed sample size and target power.
  • Added experimental, development-version-only power_achieved_calc() and power_sensitivity_calc() functions for equivalent fixed-sample analyses using calculated noncentral-F power without simulations.
  • Added power_n_calc() for calculated-power, simulation-free sample-size searches in balanced ANOVA designs.
  • Added an epsilon argument to power_n_calc() for calculated-power nonsphericity corrections on terms containing within-subject factors.
  • Added within_covariance() and a covariance argument for power_n() and power_curve() so simulations can use a custom common SD and within-subject correlation structure. These functions now derive a term-specific population Greenhouse–Geisser epsilon from that covariance and apply it to their calculated power.
  • power_curve(), power_n(), power_achieved(), power_sensitivity(), power_n_calc(), power_achieved_calc(), power_sensitivity_calc(), and design_term_means() now warn when gpower = TRUE is combined with a term whose within-subject component has more than one degree of freedom (i.e. a within factor with more than two levels). In that case target_pes under gpower = TRUE does not equal the partial eta squared actually achieved – this mirrors a property of GPower’s own “as in Cohen (1988)” repeated-measures convention, which does not adjust for the number of measurements, rather than a bug in this package (gpower = TRUE remains an exact replica of GPower’s own noncentrality formula). Use the default gpower = FALSE when target_pes should match your reported or expected partial eta squared exactly.
  • When a supplied covariance yields a population Greenhouse–Geisser epsilon below 1, power_curve(), power_n(), power_achieved(), and power_sensitivity() now base power_sim on each simulated dataset’s Greenhouse–Geisser-corrected p-value instead of the uncorrected univariate test, so power_sim and power_calc estimate the same corrected test rather than diverging under non-sphericity. This correction requires ss_type "III" or "II"; under "I", simulated p-values remain uncorrected, and these functions now warn when ss_type = "I" is combined with a covariance whose derived epsilon is below 1.

anovapowersim 1.1.0

CRAN release: 2026-05-31

  • Added a tolerance argument to power_n() for more precise control over the adaptive search.

anovapowersim 1.0.0

CRAN release: 2026-05-28

  • First official release
  • Fixed a bug where adaptive search for purely between-subjects designs would fail if the starting N was too small

anovapowersim 0.2.0

  • Added parallel processing for simulation runs in power_curve() and power_n(). Use parallel = TRUE to enable parallel simulations and cores to control the number of cores.