  The R package robusTest offers corrected versions of several common tests in
bivariate statistics. We point out the limitations of these tests in their
classical versions, some of which are well known such as robustness or
calibration problems, and provide simple alternatives that can be easily used
instead. The classical tests and theirs robust alternatives are compared
through a small simulation study. The latter emphasizes the superiority of
robust versions of the test of interest. Finally, an illustration of
correlation's tests on a real data set is also provided.
