An R package for multitrait and multienvironment data with the Item-based collaborative filtering algorithm

Abstract

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.