Design-of-Experiments toolkit (R parity)

R parity of morie.mrm_doe. Closes the Chapter-3/4/5 coverage gap from designexptr.org.

Value

Each design-of-experiments callable returns a named list holding the constructed design or the analysis result and a plain-language interpretation.

References

Box, G. E. P., Hunter, J. S., & Hunter, W. G. (2005). Statistics for Experimenters (2nd ed.). Wiley. Cochran, W. G., & Cox, G. M. (1957). Experimental Designs (2nd ed.). Wiley. Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Box, G. E. P., & Wilson, K. B. (1951). On the experimental attainment of optimum conditions. JRSS-B, 13(1), 1-45. Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences.

Examples

set.seed(2026)
n <- 30L
df <- data.frame(
  y = c(rnorm(n, 0), rnorm(n, 0.5), rnorm(n, 1)),
  g = rep(c("A", "B", "C"), each = n)
)
mrm_anova_bonferroni(df, response_col = "y", group_col = "g")$alpha_per_pair
#> [1] 0.01666667