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：建模传染病、地质现象及生物过程等复杂现实情境时常面临两难困境：计算机模型（称为模拟器）需足够复杂以捕捉系统动态，但每次复杂度提升都会延长仿真评估时间，导致难以获得与观测现实相符的参数选择的信息性描述。尽管存在优化或马尔可夫链蒙特卡洛方法等用于识别与真实观测可接受匹配的方案，但这些方法可能产生非稳健推断，或对计算密集型模拟器不可行。仿真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，同时提供关于模拟器结构的宝贵信息。然而相较于其他方法，执行仿真所需的数学考量可能对模拟器的开发者与使用者构成障碍。hmer包为在模拟器数据上应用历史匹配与仿真提供了易用框架，既利用了该方法计算效率高的优势，又使用户能够轻松匹配、可视化并稳健预测其复杂模拟器的输出结果。