The article introduces the R package *ggeffects*, which is designed to calculate and visualize marginal effects from regression models. Marginal effects are particularly useful for interpreting complex models, such as those with interaction terms or transformed variables, where direct estimates are not easily interpretable. The package aims to provide a consistent and type-safe output structure, making it easy to integrate into existing workflows. It supports a wide range of regression models, including linear, generalized linear, and mixed models, and can handle labeled data, which is beneficial for users working with data from Stata or SPSS. The main function, `ggpredict()`, allows users to specify terms for which marginal effects should be calculated, and the `plot()` method simplifies the visualization process by automatically generating plots with labeled axes and confidence bands. Examples and real-world applications are provided to demonstrate the package's utility in creating intuitive and interpretable marginal effects plots.The article introduces the R package *ggeffects*, which is designed to calculate and visualize marginal effects from regression models. Marginal effects are particularly useful for interpreting complex models, such as those with interaction terms or transformed variables, where direct estimates are not easily interpretable. The package aims to provide a consistent and type-safe output structure, making it easy to integrate into existing workflows. It supports a wide range of regression models, including linear, generalized linear, and mixed models, and can handle labeled data, which is beneficial for users working with data from Stata or SPSS. The main function, `ggpredict()`, allows users to specify terms for which marginal effects should be calculated, and the `plot()` method simplifies the visualization process by automatically generating plots with labeled axes and confidence bands. Examples and real-world applications are provided to demonstrate the package's utility in creating intuitive and interpretable marginal effects plots.