AI 中文总结
该研究提供可重现、统一且快速的软件及一站式教程，用于多种贝叶斯因子模型，能直接比较方法，为其提供通用平台，不针对特定应用推荐方法，附带factorverse R包。
AI 中文摘要
贝叶斯因子模型（BFM）是成熟的模型，用于分解一组均值为零、独立且不相关因子（随机效应）中的观测变异性。自1904年斯皮尔曼引入因子分析（FA）以来，人们对能适应现代常规收集的大型复杂数据集的推断和计算方法重新产生兴趣。我们为多种近期BFM提供可重现、统一且快速的软件，可直接比较方法，并为BFM及其实现提供一站式教程。我们不针对特定应用支持或推荐任何方法，只为BFM提供统一且可重现的通用平台。附带的factorverse R包可从此https URL获取。
英文摘要
Bayesian Factor Models (BFM) are well-established models that decompose the observed variability in a set of mean-zero, independent, and uncorrelated factors (random effects). While Factor Analysis (FA) was introduced in 1904 by Spearman, there has been renewed interest in inferential and computational methods that can adapt to large and complex modern data sets that are now routinely collected in a variety of applications. We provide reproducible, harmonized, and fast software for a variety of recent BFMs that allows the direct comparison of methods and provides a one-stop tutorial for the BFMs and their implementation. We neither endorse nor recommend any of the methods for a particular application; we simply provide a previously unavailable harmonized and reproducible common platform for BFMs. The accompanying factorverse R package is available at https://github.com/peterdunson/factorverse.
CommentsCode available at https://github.com/peterdunson/factorverse
