learnMET

Current Version: 1.1.0 (29 September 2022)

learnMET (learn Multi-Environment Trials) provides a pipeline for crop predictive breeding. In particular, learnMET (1) facilitate environmental characterization via the retrieval and aggregation of daily weather data; (2) allows the evaluation of various types of state-of-the-art machine learning approaches based on relevant cross-validation schemes for multi-environment trial datasets (3) enables to implement predictions for unobserved configurations of genotypic and environmental predictors that the user wants to test in silico.
In the Reference section, the different functions implemented in the package are listed. Only the so called main functions have to be run by the user in a typical workflow.

Installation

Install the development version from GitHub with:

devtools::install_github("cjubin/learnMET")

# To build the HTML vignette use
devtools::install_github("cjubin/learnMET", build_vignettes = TRUE)

Package documentation and vignettes

Vignettes and documentation are available at: https://cjubin.github.io/learnMET/
Vignettes are displayed under the Articles section.

Publication

A publication is available that describes the main features of the package and how to apply the different functions as a workflow. Results are provided for several Machine Learning state-of-the-art models tested with two breeding datasets:

  • learnMET: an R package to apply machine learning methods for genomic prediction using multi-environment trial data Cathy C. Westhues, Henner Simianer, Timothy M. Beissinger. G3. doi: https://doi.org/10.1093/g3journal/jkac226

Feedback

We are glad about any new user testing learnMET!
Please contact us if you encounter issues to use some functions of the package (contact: ).
Please also do not hesitate to report errors, or additional features that could be added to the package.