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gosset: An R package for analysis and synthesis of ranking data in agricultural experimentation

To derive insights from data, researchers working on agricultural experiments need appropriate data management and analysis tools. To ensure that workflows are reproducible and can be applied on a routine basis, programmatic tools are needed. Such tools are increasingly necessary for rank-based data...

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Detalles Bibliográficos
Autores principales: de Sousa, Kauê, Brown, David, Steinke, Jonathan, van Etten, Jacob
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier B.V 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10212778/
https://www.ncbi.nlm.nih.gov/pubmed/37250590
http://dx.doi.org/10.1016/j.softx.2023.101402
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author de Sousa, Kauê
Brown, David
Steinke, Jonathan
van Etten, Jacob
author_facet de Sousa, Kauê
Brown, David
Steinke, Jonathan
van Etten, Jacob
author_sort de Sousa, Kauê
collection PubMed
description To derive insights from data, researchers working on agricultural experiments need appropriate data management and analysis tools. To ensure that workflows are reproducible and can be applied on a routine basis, programmatic tools are needed. Such tools are increasingly necessary for rank-based data, a type of data that is generated in on-farm experimentation and data synthesis exercises, among others. To address this need, we developed the R package gosset, which provides functionality for rank-based data and models. The gosset package facilitates data preparation, modeling and results presentation stages. It introduces novel functions not available in existing R packages for analyzing ranking data. This paper demonstrates the package functionality using the case study of a decentralized on-farm trial of common bean (Phaseolus vulgaris L.) varieties in Nicaragua.
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spelling pubmed-102127782023-05-27 gosset: An R package for analysis and synthesis of ranking data in agricultural experimentation de Sousa, Kauê Brown, David Steinke, Jonathan van Etten, Jacob SoftwareX Original Software Publication To derive insights from data, researchers working on agricultural experiments need appropriate data management and analysis tools. To ensure that workflows are reproducible and can be applied on a routine basis, programmatic tools are needed. Such tools are increasingly necessary for rank-based data, a type of data that is generated in on-farm experimentation and data synthesis exercises, among others. To address this need, we developed the R package gosset, which provides functionality for rank-based data and models. The gosset package facilitates data preparation, modeling and results presentation stages. It introduces novel functions not available in existing R packages for analyzing ranking data. This paper demonstrates the package functionality using the case study of a decentralized on-farm trial of common bean (Phaseolus vulgaris L.) varieties in Nicaragua. Elsevier B.V 2023-05 /pmc/articles/PMC10212778/ /pubmed/37250590 http://dx.doi.org/10.1016/j.softx.2023.101402 Text en © 2023 The Author(s) https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Original Software Publication
de Sousa, Kauê
Brown, David
Steinke, Jonathan
van Etten, Jacob
gosset: An R package for analysis and synthesis of ranking data in agricultural experimentation
title gosset: An R package for analysis and synthesis of ranking data in agricultural experimentation
title_full gosset: An R package for analysis and synthesis of ranking data in agricultural experimentation
title_fullStr gosset: An R package for analysis and synthesis of ranking data in agricultural experimentation
title_full_unstemmed gosset: An R package for analysis and synthesis of ranking data in agricultural experimentation
title_short gosset: An R package for analysis and synthesis of ranking data in agricultural experimentation
title_sort gosset: an r package for analysis and synthesis of ranking data in agricultural experimentation
topic Original Software Publication
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10212778/
https://www.ncbi.nlm.nih.gov/pubmed/37250590
http://dx.doi.org/10.1016/j.softx.2023.101402
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