With this package, it is possible to compute nonparametric simultaneous
confidence intervals for relative contrast effects in the unbalanced one way layout.
Moreover, it computes simultaneous p-values. The simultaneous confidence intervals can
be computed using multivariate normal distribution, multivariate t-distribution with a Satterthwaite
Approximation of the degree of freedom or using multivariate range preserving transformations with
Logit or Probit as transformation function. 2 sample comparisons can be performed with the same
methods described above. There is no assumption on the underlying distribution function, only that
the data have to be at least ordinal numbers.

With this package, it is possible to compute nonparametric
simultaneous confidence intervals for relative contrast effects
in the unbalanced one way layout. Moreover, it computes
simultaneous p-values. The simultaneous confidence intervals
can be computed using multivariate normal distribution,
multivariate t-distribution with a Satterthwaite Approximation
of the degree of freedom or using multivariate range preserving
transformations with Logit or Probit as transformation
function. 2 sample comparisons can be performed with the same
methods described above. There is no assumption on the
underlying distribution function, only that the data have to be
at least ordinal numbers.

Perform multiple comparisons and compute simultaneous confidence
intervals for the nonparametric relative contrast effects.

Multiple Comparisons and Simultaneous Confidence Intervals
With this package, it is possible to compute nonparametric simultaneous confidence intervals for relative contrast effects in the unbalanced one way layout. Moreover, it computes simultaneous p-values. The simultaneous confidence intervals can be computed using multivariate normal distribution, multivariate t-distribution with a Satterthwaite Approximation of the degree of freedom or using multivariate range preserving transformations with Logit or Probit as transformation function. 2 sample comparisons can be performed with the same methods described above. There is no assumption on the underlying distribution function, only that the data have to be at least ordinal numbers.