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Modelling complex real-world situations such as infectious diseases,geological phenomena, and biological processes can present a dilemma: thecomputer model (referred to as a simulator) needs to be complex enough tocapture the dynamics of the system, but each increase in complexity increasesthe evaluation time of such a simulation, making it difficult to obtain aninformative description of parameter choices that would be consistent withobserved reality. While methods for identifying acceptable matches toreal-world observations exist, for example optimisation or Markov chain MonteCarlo methods, they may result in non-robust inferences or may be infeasiblefor computationally intensive simulators. The techniques of emulation andhistory matching can make such determinations feasible, efficiently identifyingregions of parameter space that produce acceptable matches to data while alsoproviding valuable information about the simulator's structure, but themathematical considerations required to perform emulation can present a barrierfor makers and users of such simulators compared to other methods. The hmerpackage provides an accessible framework for using history matching andemulation on simulator data, leveraging the computational efficiency of theapproach while enabling users to easily match to, visualise, and robustlypredict from their complex simulators.

Emulation and History Matching using the hmer Package