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：对传染病、地质现象及生物过程等复杂现实情境进行建模存在两难：计算机模型（简称模拟器）需具备足够复杂度以捕捉系统动态，但复杂度每增加一次，模拟评估时间就会延长，导致难以获得与观测现实相一致的参数选择描述性信息。虽存在优化或马尔可夫链蒙特卡洛方法等识别与真实观测可接受匹配的技术，但这些方法可能产生非鲁棒性推断，或对计算密集型模拟器而言不可行。仿真与历史匹配技术可使此类判定具备可行性，既能高效识别与数据产生可接受匹配的参数空间区域，又能提供模拟器结构的宝贵信息；但与其他方法相比，执行仿真所需的数学考量可能为这类模拟器的开发者和使用者设置障碍。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，既利用该方法计算高效性的优势，又使用户能够轻松匹配、可视化并对其复杂模拟器进行鲁棒性预测。