Uniformly Most Powerful Invariant Tests of Equivalence
person("Thoralf", "Mildenberger", email = "mild@zhaw.ch", role = c("aut", "cre"))
Implementation of uniformly most powerful invariant equivalence
tests for one- and two-sample problems (paired and unpaired) as described
in Wellek (2010, ISBN:978-1-4398-0818-4). Also one-sided alternatives
(non-inferiority and non-superiority tests) are supported. Basically a variant
of a t-test with (relaxed) null and alternative hypotheses exchanged.

Provides tests and graphics for assessing tests of equivalence
This package provides some statistical tests and graphics for assessing tests of equivalence.  Such tests have similarity as the alternative hypothesis instead of the null.  Sample datasets are included.

This package provides some statistical tests and graphics
for assessing tests of equivalence.  Such tests have similarity
as the alternative hypothesis instead of the null.  Sample
datasets are included.

This package provides some statistical tests and graphics
for assessing tests of equivalence.  Such tests have similarity
as the alternative hypothesis instead of the null.  Sample
data sets are included.

Provides Tests and Graphics for Assessing Tests of Equivalence
Provides statistical tests and graphics for assessing tests
of equivalence.  Such tests have similarity as the alternative
hypothesis instead of the null.  Sample data sets are included.

Equivalence Test for the Means of Two Normal Distributions
person("Chao", "Wang", email = "wan9c9@gmail.com", role = c("aut",  "cre"))
Two methods for performing equivalence test for the means of two (test and reference) normal distributions are implemented. The null hypothesis of the equivalence test is that the absolute difference between the two means are greater than or equal to the equivalence margin and the alternative is that the absolute difference is less than the margin. Given that the margin is often difficult to obtain a priori, it is assumed to be a constant multiple of the standard deviation of the reference distribution. The first method assumes a fixed margin which is a constant multiple of the estimated standard deviation of the reference data and whose variability is ignored. The second method takes into account the margin variability. In addition, some tools to summarize and illustrate the data and test results are included to facilitate the evaluation of the data and interpretation of the results.
polynom, rootSolve, cubature, Rdpack, stats, graphics