When doing empirical studies in the field of language evolution, change over time is an inherent dimension. This tutorial introduces readers to mixed models, Growth Curve Analysis (GCA) and Generalized Additive Models (GAMs). These approaches are ideal for analyzing nonlinear change over time where there are nested dependencies, such as time points within dyad (in repeated interaction experiments) or time points within chain (in iterated learning experiments). In addition, the tutorial gives recommendations for choices about model fitting. Annotated scripts in the online [Supplementary Data][1] provide the reader with R code to serve as a springboard for the reader’s own analyses. [1]: http://jole.oxfordjournals.org/lookup/suppl/doi:10.1093/jole/lzv003/-/DC1

中文翻译：

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如何使用混合模型、增长曲线分析和广义加性模型分析语言变化

在语言进化领域进行实证研究时，随时间的变化是一个固有的维度。本教程向读者介绍了混合模型、增长曲线分析 (GCA) 和广义加性模型 (GAM)。这些方法非常适合分析存在嵌套依赖关系的非线性变化，例如 dyad 中的时间点（在重复交互实验中）或链中的时间点（在迭代学习实验中）。此外，本教程还提供了有关模型拟合选择的建议。在线[补充数据][1]中的注释脚本为读者提供了R代码，作为读者自己分析的跳板。[1]：http://jole.oxfordjournals.org/lookup/suppl/doi:10.1093/jole/lzv003/-/DC1