  Cosmological parameter inference has been dominated by the Bayesian approach
for the past two decades, primarily due to its computational efficiency.
However, the Bayesian approach involves integration of the posterior
probability and therefore depends on both the choice of model parametrisation
and the choice of prior on the model parameter space. In some cases, this can
lead to conclusions which are driven by choice of parametrisation and priors
rather than by data. The profile likelihood method provides a complementary
frequentist tool which can be used to investigate this effect.
  In this paper, we present the code PROSPECT for computing profile likelihoods
in cosmology. We showcase the code using a phenomenological model for
converting dark matter into dark radiation that suffers from large volume
effects and prior dependence. PROSPECT is compatible with both cobaya and
MontePython, and is publicly available at
https://github.com/AarhusCosmology/prospect_public.
