  Modelling complex real-world situations such as infectious diseases,
geological phenomena, and biological processes can present a dilemma: the
computer model (referred to as a simulator) needs to be complex enough to
capture the dynamics of the system, but each increase in complexity increases
the evaluation time of such a simulation, making it difficult to obtain an
informative description of parameter choices that would be consistent with
observed reality. While methods for identifying acceptable matches to
real-world observations exist, for example optimisation or Markov chain Monte
Carlo methods, they may result in non-robust inferences or may be infeasible
for computationally intensive simulators. The techniques of emulation and
history matching can make such determinations feasible, efficiently identifying
regions of parameter space that produce acceptable matches to data while also
providing valuable information about the simulator's structure, but the
mathematical considerations required to perform emulation can present a barrier
for makers and users of such simulators compared to other methods. The hmer
package provides an accessible framework for using history matching and
emulation on simulator data, leveraging the computational efficiency of the
approach while enabling users to easily match to, visualise, and robustly
predict from their complex simulators.
