A newly emerging field in statistics is distributional regression, where not only the mean but each parameter of a parametric response distribution can be modelled using a set of predictors. As an extension of generalized additive models, distributional regression utilizes the known link functions (log, logit, etc.), model terms (fixed, random, spatial, smooth, etc.) and available types of distributions but allows us to go well beyond the exponential family and to model potentially all distributional parameters. Due to this increase in model flexibility, the interpretation of covariate effects on the shape of the conditional response distribution, its moments and other features derived from this distribution is more challenging than with traditional mean-based methods. In particular, such quantities of interest often do not directly equate the modelled parameters but are rather a (potentially complex) combination of them. To ease the post-estimation model analysis, we propose a framework and subsequently feature an implementation in R for the visualization of Bayesian and frequentist distributional regression models fitted using the bamlss, gamlss and betareg R packages.

统计学中的一个新兴领域是分布回归，其中不仅可以使用一组预测器对参数响应分布的平均值而且可以对每个参数进行建模。作为广义加性模型的扩展，分布回归利用已知的链接函数（对数，对数等），模型项（固定，随机，空间，平滑等）和可用的分布类型，但允许我们超越指数族，并可能对所有分布参数进行建模。由于模型灵活性的提高，对条件响应分布的形状，其矩和从该分布派生的其他特征的协变量影响的解释比传统的基于均值的方法更具挑战性。特别是，这样感兴趣的数量通常不直接等于建模参数，而是它们的一个（可能很复杂的）组合。为了简化估算后的模型分析，我们提出了一个框架，并随后在R中引入了一个实现，以可视化使用bamlss，gamlss和betareg R软件包安装的贝叶斯和常客分布回归模型。