{"title":"edibble:一个R包,用于封装实验设计的元素,以便更好地规划、管理和工作流程","authors":"Emi Tanaka","doi":"arxiv-2311.09705","DOIUrl":null,"url":null,"abstract":"I present an R package called edibble that facilitates the design of\nexperiments by encapsulating elements of the experiment in a series of\ncomposable functions. This package is an interpretation of \"the grammar of\nexperimental designs\" by Tanaka (2023) in the R programming language. The main\nfeatures of the edibble package are demonstrated, illustrating how it can be\nused to create a wide array of experimental designs. The implemented system\naims to encourage cognitive thinking for holistic planning and data management\nof experiments in a streamlined workflow. This workflow can increase the\ninherent value of experimental data by reducing potential errors or noise with\ncareful preplanning, as well as, ensuring fit-for-purpose analysis of\nexperimental data.","PeriodicalId":501323,"journal":{"name":"arXiv - STAT - Other Statistics","volume":"1 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"edibble: An R package to encapsulate elements of experimental designs for better planning, management and workflow\",\"authors\":\"Emi Tanaka\",\"doi\":\"arxiv-2311.09705\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"I present an R package called edibble that facilitates the design of\\nexperiments by encapsulating elements of the experiment in a series of\\ncomposable functions. This package is an interpretation of \\\"the grammar of\\nexperimental designs\\\" by Tanaka (2023) in the R programming language. The main\\nfeatures of the edibble package are demonstrated, illustrating how it can be\\nused to create a wide array of experimental designs. The implemented system\\naims to encourage cognitive thinking for holistic planning and data management\\nof experiments in a streamlined workflow. This workflow can increase the\\ninherent value of experimental data by reducing potential errors or noise with\\ncareful preplanning, as well as, ensuring fit-for-purpose analysis of\\nexperimental data.\",\"PeriodicalId\":501323,\"journal\":{\"name\":\"arXiv - STAT - Other Statistics\",\"volume\":\"1 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-11-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - STAT - Other Statistics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2311.09705\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - STAT - Other Statistics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2311.09705","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
edibble: An R package to encapsulate elements of experimental designs for better planning, management and workflow
I present an R package called edibble that facilitates the design of
experiments by encapsulating elements of the experiment in a series of
composable functions. This package is an interpretation of "the grammar of
experimental designs" by Tanaka (2023) in the R programming language. The main
features of the edibble package are demonstrated, illustrating how it can be
used to create a wide array of experimental designs. The implemented system
aims to encourage cognitive thinking for holistic planning and data management
of experiments in a streamlined workflow. This workflow can increase the
inherent value of experimental data by reducing potential errors or noise with
careful preplanning, as well as, ensuring fit-for-purpose analysis of
experimental data.