This method provides 95 percent simultaneous confidence interval for multinomial proportions based on Bayesian Multinomial Dirichlet model. However, it provides a mechanism through which user can split the Dirichlet prior parameter vector and suitable distributions can be incorporated for each of two groups.

scimp_bmdu(x, d, seed = 1492)



cell counts of given contingency table corresponding to a categorical data - non negative integers


number of divisions required to split the prior vector of Dirichlet distribution to assign unequal values from U(0,1) and U(1,2)


random seed for reproducible results


tibble with original limits of multinomial proportions together with product of length of k intervals as volume of simultaneous confidence intervals and the mean


Gelman, A., Carlin, J.B., Stern, H.S., and Rubin, D.B. (2002). Bayesian Data Analysis. Chapman & Hall, London.


y <- c(44, 55, 43, 32, 67, 78)
z <- 2
scimp_bmdu(y, z)