Two fundamental research tasks in science and engineering are forward predictions and data inversion. This article introduces a recent R package RobustCalibration for Bayesian data inversion and model calibration by experiments and field observations. Mathematical models for forward predictions are often written in computer code, and they can be computationally expensive slow to run. To overcome the computational bottleneck from the simulator, we implemented a statistical emulator from the RobustGaSP package for emulating both scalar-valued or vector-valued computer model outputs. Both posterior sampling and maximum likelihood approach are implemented in the RobustCalibration package for parameter estimation. For imperfect computer models, we implement Gaussian stochastic process and the scaled Gaussian stochastic process for modeling the discrepancy function between the reality and mathematical model. This package is applicable to various types of field observations, such as repeated experiments and multiple sources of measurements. We discuss numerical examples of calibrating mathematical models that have closed-form expressions, and differential equations solved by numerical methods.

翻译：科学与工程领域的两个基础研究任务是正向预测与数据反演。本文介绍了一个最新的R语言软件包RobustCalibration，用于基于实验与现场观测的贝叶斯数据反演与模型校准。用于正向预测的数学模型通常以计算机代码形式实现，且可能因计算成本高昂而运行缓慢。为克服模拟器的计算瓶颈，我们采用RobustGaSP包中的统计仿真器，对标量或向量输出的计算机模型输出进行仿真。RobustCalibration包同时实现了后验采样与最大似然估计两种参数估计方法。针对非完美计算机模型，我们采用高斯随机过程与缩放高斯随机过程对现实与数学模型之间的失配函数进行建模。该软件包适用于多种类型的现场观测数据，例如重复实验与多源测量。我们通过数值案例展示了校准具有闭式表达式数学模型的示例，以及通过数值方法求解微分方程的案例。