Power Analyses using Monte Carlo Simulations
Get previously evaluated Spower execution
Evaluate whether a confidence interval is within a tolerable interval
Evaluate whether parameter is outside a given confidence interval
p-value from comparing two or more correlations simulation
p-value from one-way ANOVA simulation
p-value from chi-squared test simulation
p-value from (generalized) linear regression model simulations with fi...
p-value from Kruskal-Wallis Rank Sum Test simulation
p-value from Kolmogorov-Smirnov one- or two-sample simulation
p-value from global linear regression model simulation
p-value from Mauchly's Test of Sphericity simulation
p-value from McNemar test simulation
p-value from three-variable mediation analysis simulation
p-value from proportion test simulation
p-value from tetrachoric/polychoric or polyserial
p-value from correlation simulation
p-value from Scale Test simulation
p-value from Shapiro-Wilk Normality Test simulation
p-value from simple linear regression model simulation
p-value from independent/paired samples t-test simulation
p-value from variance test simulation
p-value from Wilcoxon (signed rank) test simulation
Simulation-based Power Analyses
Update compromise or prospective/post-hoc power analysis without re-si...
Provides a general purpose simulation-based power analysis API for routine and customized simulation experimental designs. The package focuses exclusively on Monte Carlo simulation experiment variants of (expected) prospective power analyses, criterion analyses, compromise analyses, sensitivity analyses, and a priori/post-hoc analyses. The default simulation experiment functions defined within the package provide stochastic variants of the power analysis subroutines in G*Power 3.1 (Faul, Erdfelder, Buchner, and Lang, 2009) <doi:10.3758/brm.41.4.1149>, along with various other parametric and non-parametric power analysis applications (e.g., mediation analyses) and support for Bayesian power analysis by way of Bayes factors or posterior probability evaluations. Additional functions for building empirical power curves, reanalyzing simulation information, and for increasing the precision of the resulting power estimates are also included, each of which utilize similar API structures. For further details see the associated publication in Chalmers (2025) <doi:10.3758/s13428-025-02787-z>.
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