The R package glmm enables likelihood‐based inference for generalized linear mixed models with a canonical link. No other publicly available software accurately conducts likelihood‐based inference for generalized linear mixed models with crossed random effects. glmm is able to do so by approximating the likelihood function and two derivatives using importance sampling. The importance sampling distribution is an essential piece of Monte Carlo likelihood approximation, and developing a good one is the main challenge in implementing it. The package glmm uses the data to tailor the importance sampling distribution and is constructed to ensure finite Monte Carlo standard errors. In the context of the generalized linear mixed model, the salamander model with crossed random effects has become a benchmark example. We use this model to illustrate the complexities of the likelihood function and to demonstrate the use of the R package glmm.

中文翻译：

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广义线性混合模型的基于似然性的推理：使用R包glmm进行推理

所述ř包GLMM使广义线性混合模型与规范链接基于似然的推断。没有其他公开可用的软件能够对具有交叉随机效应的广义线性混合模型进行准确的基于似然性的推断。glmm能够通过使用重要性采样来近似似然函数和两个导数来做到这一点。重要度采样分布是蒙特卡罗似然近似的重要组成部分，开发一个好的重要性分布是实现它的主要挑战。包glmm使用数据来调整重要性抽样分布，并构造为确保有限的蒙特卡洛标准误差。在广义线性混合模型的背景下，具有交叉随机效应的sal模型已成为基准示例。我们使用此模型来说明似然函数的复杂性，并演示R包glmm的使用。