The Item‐Based Collaborative Filtering for Multitrait and Multienvironment Data (IBCF.MTME) package was developed to implement the item‐based collaborative filtering (IBCF) algorithm for continuous phenotypic data in the context of plant breeding where data are collected for various traits and environments. The main difference between this package and the other available packages that can implement IBCF is that this one was developed for continuous phenotypic data, which cannot be implemented in the current packages because they can implement IBCF only for binary and ordinary phenotypes. In the following article, we will show how to both install the package and use it for studying the prediction accuracy of multitrait and multienvironment data under phenotypic and genomic selection. We illustrate its use with seven examples (with information from two datasets, Wheat_IBCF and Year_IBCF, which are included in the package) comprising multienvironment data, multitrait data, and both multitrait and multienvironment data that cover scenarios in which breeding scientists are interested. The package offers many advantages for studying the genomic‐enabled prediction accuracy of multitrait and multienvironment data, ultimately helping plant breeders make better decisions.

中文翻译：

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使用基于项目的协作过滤算法的多特征和多环境数据的R包。

开发了基于项目的多性状和多环境数据协同过滤（IBCF.MTME）包，以在植物育种的环境中实现针对连续表型数据的基于项目的协同过滤（IBCF）算法，在该育种中收集了各种性状和环境的数据。此程序包与其他可以实现IBCF的程序包之间的主要区别在于，该程序包是为连续表型数据开发的，由于它们只能为二进制和普通表型实现IBCF，因此无法在当前程序包中实现。在下面的文章中，我们将展示如何安装该软件包并将其用于研究在表型和基因组选择下的多性状和多环境数据的预测准确性。我们通过七个示例（包含来自软件包中包含的两个数据集Wheat_IBCF和Year_IBCF的信息）来说明其用法，这些示例包含多环境数据，多特征数据以及涵盖育种科学家感兴趣的场景的多特征和多环境数据。该软件包为研究基因组预测的多性状和多环境数据的准确性提供了许多优势，最终帮助植物育种者做出更好的决策。