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.

翻译：模拟复杂现实情境（如传染病、地质现象和生物过程）会面临一个两难困境：计算机模型（称为仿真器）需要足够复杂以捕捉系统动态，但每次复杂度提升都会增加仿真评估时间，从而难以获得与观测现实一致且具有信息量的参数选择描述。尽管存在识别可接受现实观测匹配的方法（例如优化或马尔可夫链蒙特卡洛方法），但这些方法可能导致非稳健的推断，或对计算密集型仿真器而言不可行。仿真模拟与历史匹配技术可使此类判定变得可行，高效识别能产生与数据可接受匹配的参数空间区域，同时提供关于仿真器结构的有价值信息，但与其他方法相比，执行仿真模拟所需的数学考量可能成为此类仿真器制造者和使用者的障碍。hmer包为在仿真器数据上应用历史匹配与仿真模拟提供了一个易用框架，既利用了该方法的高计算效率，又使用户能够轻松匹配、可视化并稳健预测其复杂仿真器。