The integrated nested Laplace approximation (INLA) method has become a popular approach for computationally efficient approximate Bayesian computation. In particular, by leveraging sparsity in random effect precision matrices, INLA is commonly used in spatial and spatio-temporal applications. However, the speed of INLA comes at the cost of restricting the user to the family of latent Gaussian models and the likelihoods currently implemented in {INLA}, the main software implementation of the INLA methodology. {inlabru} is a software package that extends the types of models that can be fitted using INLA by allowing the latent predictor to be non-linear in its parameters, moving beyond the additive linear predictor framework to allow more complex functional relationships. For inference it uses an approximate iterative method based on the first-order Taylor expansion of the non-linear predictor, fitting the model using INLA for each linearised model configuration. {inlabru} automates much of the workflow required to fit models using {R-INLA}, simplifying the process for users to specify, fit and predict from models. There is additional support for fitting joint likelihood models by building each likelihood individually. {inlabru} also supports the direct use of spatial data structures, such as those implemented in the {sf} and {terra} packages. In this paper we outline the statistical theory, model structure and basic syntax required for users to understand and develop their own models using {inlabru}. We evaluate the approximate inference method using a Bayesian method checking approach. We provide three examples modelling simulated spatial data that demonstrate the benefits of the additional flexibility provided by {inlabru}.

翻译：集成嵌套拉普拉斯近似（INLA）方法已成为计算高效近似贝叶斯计算的一种流行方法。特别是通过利用随机效应精度矩阵的稀疏性，INLA 在空间和时空应用中得到了广泛使用。然而，INLA 的速度是以限制用户使用潜高斯模型族及当前 {INLA} 软件（INLA 方法的主要实现）中已实现的似然函数为代价的。{inlabru} 是一个软件包，它通过允许潜预测因子在其参数上呈现非线性，扩展了可使用 INLA 拟合的模型类型，突破了加性线性预测因子的框架，从而支持更复杂的函数关系。在推断方面，它采用基于非线性预测因子一阶泰勒展开的近似迭代方法，对每个线性化模型配置使用 INLA 进行拟合。{inlabru} 自动化了使用 {R-INLA} 拟合模型所需的大部分工作流程，简化了用户指定模型、拟合模型及从模型进行预测的过程。该软件还额外支持通过单独构建每个似然函数来拟合联合似然模型。{inlabru} 还支持直接使用空间数据结构，例如 {sf} 和 {terra} 包中实现的数据结构。本文概述了用户理解并使用 {inlabru} 开发自身模型所需的统计理论、模型结构及基本语法。我们采用贝叶斯方法检验策略评估了该近似推断方法。通过三个模拟空间数据建模示例，我们展示了 {inlabru} 所提供的额外灵活性带来的优势。