Statistical inference is a major scientific endeavor for many researchers. In terms of inferential methods implemented to mixed-effects models, significant progress has been made in the R software. However, these advances primarily concern classical estimators (ML, REML) and mainly focus on fixed effects. In the confintROB package, we have implemented various bootstrap methods for computing confidence intervals (CIs) not only for fixed effects but also for variance components. These methods can be implemented with the widely used lmer function from the lme4 package, as well as with the rlmer function from the robustlmm package and the varComprob function from the robustvarComp package. These functions implement robust estimation methods suitable for data with outliers. The confintROB package implements the Wald method for fixed effects, whereas for both fixed effects and variance components, two bootstrap methods are implemented: the parametric bootstrap and the wild bootstrap. Moreover, the confintROB package can obtain both the percentile and the bias-corrected accelerated versions of CIs.

翻译：统计推断是许多研究人员的重要科学工作。在混合效应模型中实施的推断方法方面，R软件已取得显著进展。然而，这些进展主要涉及经典估计量（ML、REML），并主要聚焦于固定效应。在confintROB包中，我们实现了多种自举方法，用于计算固定效应和方差分量的置信区间。这些方法可配合lme4包中广泛使用的lmer函数、robustlmm包中的rlmer函数以及robustvarComp包中的varComprob函数使用。这些函数实现了适用于含异常值数据的稳健估计方法。confintROB包对固定效应实施了Wald方法，而对固定效应和方差分量均实施了两种自举方法：参数自举和wild自举。此外，confintROB包可同时获取置信区间的百分位数版本和偏差校正加速版本。