gLike: numerical maximization of heterogeneous joint likelihood functions of a common free parameter plus nuisance parameters

gLike is a general-purpose ROOT-based code framework for the numerical maximization of joint likelihood functions.

The joint likelihood function has one free parameter (named g) and as many nuisance parameters as wanted, which will be profiled in the  maximization process. 

Follows a non-exhaustive list of examples where gLike is useful (in order of increasingly complexity):

 - Estimating the number of signal events (with uncertainties) in a dataset whose background content is in turn estimated from an independent measurement in a signal-free control-region.
 - Same as before, but considering in addition a systematic uncertainty in the estimation of the background. 
 - Estimating the intensity of a steady source of signal particles in the presence of background particles from datasets obtained under different experimental conditions.
 - Same as before, but each dataset actually comes from a different instrument and in different data format.
 - Estimating the dark matter annihilation cross-section combining observations of dwarf spheroidal galaxies by different ground-based gamma-ray telescopes, satellite gamma-ray detectors, neutrino telescopes, ....
 - Estimating the energy scale of quantum gravity by combining observations of fast gamma-ray flares observed by different ground-based gamma-ray telescopes.
 - ...