Agnostic cosmology in the CAMEL framework
  Cosmological parameter estimation is traditionally performed in the Bayesian
context. By adopting an "agnostic" statistical point of view, we show the
interest of confronting the Bayesian results to a frequentist approach based on
profile-likelihoods. To this purpose, we have developed the Cosmological
Analysis with a Minuit Exploration of the Likelihood ("CAMEL") software.
Written from scratch in pure C++, emphasis was put in building a clean and
carefully-designed project where new data and/or cosmological computations can
be easily included.
  CAMEL incorporates the latest cosmological likelihoods and gives access from
the very same input file to several estimation methods: (i) A high quality
Maximum Likelihood Estimate (a.k.a "best fit") using MINUIT ; (ii) profile
likelihoods, (iii) a new implementation of an Adaptive Metropolis MCMC
algorithm that relieves the burden of reconstructing the proposal distribution.
  We present here those various statistical techniques and roll out a full
use-case that can then used as a tutorial. We revisit the $\Lambda$CDM
parameters determination with the latest Planck data and give results with both
methodologies. Furthermore, by comparing the Bayesian and frequentist
approaches, we discuss a "likelihood volume effect" that affects the optical
reionization depth when analyzing the high multipoles part of the Planck data.
  The software, used in several Planck data analyzes, is available from
http://camel.in2p3.fr. Using it does not require advanced C++ skills.
