https://github.com/breeding-insight/mrbeanapp
Web application
Science Score: 13.0%
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○CITATION.cff file
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○codemeta.json file
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○.zenodo.json file
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✓DOI references
Found 4 DOI reference(s) in README -
○Academic publication links
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○Scientific vocabulary similarity
Low similarity (11.8%) to scientific vocabulary
Last synced: 10 months ago
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JSON representation
Repository
Web application
Basic Info
- Host: GitHub
- Owner: Breeding-Insight
- License: other
- Language: R
- Default Branch: main
- Homepage: https://apariciojohan.github.io/MrBeanApp/
- Size: 25.1 MB
Statistics
- Stars: 0
- Watchers: 0
- Forks: 1
- Open Issues: 1
- Releases: 0
Fork of AparicioJohan/MrBeanApp
Created over 2 years ago
· Last pushed about 1 year ago
https://github.com/Breeding-Insight/MrBeanApp/blob/main/
# MrBean[](https://www.tidyverse.org/lifecycle/#experimental) Mr. Bean is an easy to use R-Shiny web-app that simplifies the analysis of large-scale plant breeding experimental analysis by using the power and versatility of Linear Mixed Models (LMM). This app combines the analytical robustness and speed of ASReml and SpATS with the visual power offered by R. Mr. Bean provides a graphical workflow for importing data, identifying outliers, and fitting field data using LMM with or without spatial correction. The results are BLUPs/BLUEs predictions and heritabilities for single-environmental experiments or multiple-environmental trial (MET) analysis. In addition, Mr. Bean also provides a module for exploring results from METs using several graphical and multivariate techniques. https://apariciojohan.github.io/MrBeanApp/ ## Installation You can install the package: ``` r devtools::install_github("AparicioJohan/MrBeanApp") ``` or ```r remotes::install_github("AparicioJohan/MrBeanApp") ``` ## Example ``` r library(MrBean) run_app() ``` ## Demo A running demo is on [shinyapps.io](https://beanteam.shinyapps.io/MrBean_BETA/). ### Citation > *Aparicio J, Gezan SA, Ariza-Suarez D, Raatz B, Diaz S, Heilman-Morales A and Lobaton J (2024)* Mr.Bean: a comprehensive statistical and visualization application for modeling agricultural field trials data. **Front. Plant Sci. 14:1290078.**; [https://doi.org/10.3389/fpls.2023.1290078](https://doi.org/10.3389/fpls.2023.1290078) ### Acknowledgments > * [Bean Breeding Program - International Center for Tropical Agriculture (CIAT)](https://alliancebioversityciat.org/) > * [Big Data Pipeline Unit (NDSU)](https://sites.google.com/ndsu.edu/plsc-bpdm/home)
Please note that the MrBean project is released with a [Contributor Code of Conduct](CODE_OF_CONDUCT.md). By contributing to this project, you agree to abide by its terms.
Owner
- Name: Breeding Insight
- Login: Breeding-Insight
- Kind: organization
- Website: https://breedinginsight.org
- Repositories: 12
- Profile: https://github.com/Breeding-Insight
Combining genomics and informatics to accelerate genetic gains.
GitHub Events
Total
- Member event: 1
- Push event: 7
- Pull request event: 2
- Fork event: 1
- Create event: 1
Last Year
- Member event: 1
- Push event: 7
- Pull request event: 2
- Fork event: 1
- Create event: 1
[](https://www.tidyverse.org/lifecycle/#experimental)
Mr. Bean is an easy to use R-Shiny web-app that simplifies the analysis of large-scale plant breeding experimental analysis by using the power and versatility of Linear Mixed Models (LMM). This app combines the analytical robustness and speed of ASReml and SpATS with the visual power offered by R. Mr. Bean provides a graphical workflow for importing data, identifying outliers, and fitting field data using LMM with or without spatial correction. The results are BLUPs/BLUEs predictions and heritabilities for single-environmental experiments or multiple-environmental trial (MET) analysis. In addition, Mr. Bean also provides a module for exploring results from METs using several graphical and multivariate techniques.
