solomonR 0.4.0

solomonR 0.4.0

solomonR 0.4.0 adds validated power and sample-size planning, figures designed for the Solomon four-group design, and a single index of the package's simulation evidence.

Highlights

Power and sample-size planning

• power_solomon() is rebuilt and validated (#18).
  • For each Solomon test it reports the rejection rate with its Monte Carlo standard error.
  • Every row names the estimand and the true effect behind it.
  • New alpha and seed arguments.
  • A pre-specified simulation study (126 scenarios, 315,000 replications) found:
    • exact agreement with the analytic benchmark for the 2x2 ANOVA interaction;
    • nominal size for Test I under the complete null;
    • no fit failures.
  • GLM-based rates are conservative with 20 or fewer participants per cell, following the HC3 standard errors used by default.
• New plan_solomon() (#24). It finds the smallest design, at a chosen allocation across the four cells, whose power reaches a target for each estimand.
  • It works analytically, returning the exact minimum, or by simulating the package's own GLM test.
  • The help page shows that the sensitization contrast has four times the sampling variance of the average treatment effect, so detecting sensitization as large as the average effect needs about four times as many participants.

Figures

Function	Issue	What it shows
plot_solomon_design()	#25	The design in Campbell and Stanley's notation, labeled with group sizes and posttest means, with empty or sparse groups flagged
plot_sensitization()	#26	Model-adjusted group means whose interaction equals the fitted sensitization contrast exactly, with observed means for comparison
plot_solomon_effects()	#27	A forest plot of the four Solomon contrasts, stating the confidence level and reference distribution
plot_solomon_change()	#28	Pretest-to-posttest change for the pretested groups, with the unpretested groups labeled as posttest-only by design
plot_classic_flow()	#29	The historical Tests A-I decision path, highlighting a fitted analysis's route, with its Type I error caution
plot_power_solomon()	#30	Power curves by estimand, marking the designs plan_solomon() returns

Evidence

• New article: "Validation Evidence" (#11). It gathers the package's simulation studies, with each study's protocol, scenarios, replications, failed fits, and results against its pre-registered tolerances.
• Shared tables. studies.csv and benchmarks.csv are rebuilt from each study's committed results and can be downloaded from the repository.

Changes that can alter results

• power_solomon() returns a new data frame.
  • The columns estimand, test, true_effect, power, mcse, sims, failures, and alpha replace the former metric and power.
  • The experimental warning is removed.
• Wider cell-mean intervals in two plots. plot_solomon() and plot_solomon_gg() now use t intervals with n - 1 degrees of freedom for cell means instead of the normal quantile, so the intervals are slightly wider with small cells.

Checks

• R CMD check: 0 errors, 0 warnings, 0 notes on Windows, macOS, and Ubuntu (R release, oldrel-1, and devel; devel also with --as-cran).
• Check run: https://github.com/JUhalt/solomonR/actions/runs/36130773705
• pkgdown site: https://juhalt.github.io/solomonR/

Installation

install.packages(
  "solomonR",
  repos = c(
    JUhalt = "https://juhalt.r-universe.dev",
    CRAN = "https://cloud.r-project.org"
  )
)

or

pak::pak("JUhalt/solomonR@v0.4.0")

Not in this release

• plot_sensitization() for fit_solomon_ml() fits. The ML small-sample degrees-of-freedom computation must be exposed first.
• Binary and count outcomes, clustered and longitudinal designs, and cluster-level randomization inference (#19). These are planned for v0.5.0.