inlamemi：一个使用INLA进行缺失数据插补和测量误差建模的R包
inlamemi: An R package for missing data imputation and measurement error modelling using INLA
AI总结：
本文介绍inlamemi R包，利用INLA在贝叶斯框架下对连续协变量中的测量误差和缺失数据进行分层建模，降低使用门槛并提供多种场景示例。
摘要：
统计模型所用变量中的测量误差和缺失数据十分常见，若忽视这些问题，最严重时可能导致分析产生严重偏差。然而，这些问题往往未得到充分处理，部分原因可能是分析人员缺乏足够简单的工具来考虑误差和缺失。在本R包中，我们提供函数以辅助拟合分层贝叶斯模型，用于处理连续协变量中存在测量误差（经典或Berkson）、缺失数据或两者兼有的情况。模型拟合在贝叶斯框架下使用集成嵌套拉普拉斯近似（INLA）完成，该方法因其计算速度与准确性的结合而日益流行。{inlamemi} R包适用于对使用{R-INLA} R包几乎没有经验的数据分析人员，并有助于为各种场景制定合适的分层模型，以恰当捕捉产生测量误差和/或缺失的机制。文中给出了大量示例，帮助分析人员识别与自身情况相似的场景，并使指定合适模型的过程更加容易。
英文摘要：
Measurement error and missing data in variables used in statistical models are common, and can at worst lead to serious biases in analyses if they are ignored. Yet, these problems are often not dealt with adequately, presumably in part because analysts lack simple enough tools to account for error and missingness. In this R package, we provide functions to aid fitting hierarchical Bayesian models that account for cases where either measurement error (classical or Berkson), missing data, or both are present in continuous covariates. Model fitting is done in a Bayesian framework using integrated nested Laplace approximations (INLA), an approach that is growing in popularity due to its combination of computational speed and accuracy. The {inlamemi} R package is suitable for data analysts who have little prior experience using the R package {R-INLA}, and aids in formulating suitable hierarchical models for a variety of scenarios in order to appropriately capture the processes that generate the measurement error and/or missingness. Numerous examples are given to help analysts identify scenarios similar to their own, and make the process of specifying a suitable model easier.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
