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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
```
# **learnMET**
### **Current Version**: 1.1.0 (29 September 2022)
[](https://doi.org/10.1093/g3journal/jkac226)
[](https://packagist.org/packages/cjubin/learnMET)
[](https://GitHub.com/Naereen/StrapDown.js/tags/)
`learnMET` (**learn** **M**ulti-**E**nvironment **T**rials) 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](https://github.com/cjubin/learnMET) with:
```{r, eval=FALSE}
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: [email protected]).\
Please also do not hesitate to report errors, or additional features that could be added to the package.