{ "_id": "6a1f0d6bb401979e7341d024", "Type": "Package", "Package": "dabestr", "Title": "Data Analysis using Bootstrap-Coupled Estimation", "Version": "2025.3.14", "Authors@R": "c(\nperson(\"Joses W.\", \"Ho\", , \"joseshowh@gmail.com\", role = \"aut\",\ncomment = c(ORCID = \"0000-0002-9186-6322\")),\nperson(\"Kah Seng\", \"Lian\", , \"kahseng@u.nus.edu\", role = \"aut\"),\nperson(\"Ana Rosa\", \"Castillo\", , \"arosacastillo@protonmail.com\", role = \"aut\"),\nperson(\"Zhuoyu\", \"Wang\", , \"wzhuoyu21@u.nus,edu\", role = \"aut\"),\nperson(\"Jun Yang\", \"Liao\", , \"name@example.com\", role = \"aut\"),\nperson(\"Felicia\", \"Low\", , \"lowminhuifelicia@gmail.com\", role = \"aut\"),\nperson(\"Tayfun\", \"Tumkaya\", , role = \"aut\",\ncomment = c(ORCID = \"0000-0001-8425-3360\")),\nperson(\"Jonathan\", \"Anns\", , role = \"ctb\",\ncomment = c(ORCID = \"0009-0005-8349-4986\")),\nperson(\"Yishan\", \"Mai\", ,\"maiyishan@u.duke.nus.edu\", role = c(\"cre\", \"ctb\"),\ncomment = c(ORCID = \"0000-0002-7199-380X\")),\nperson(\"Sangyu\", \"Xu\", , role = \"ctb\",\ncomment = c(ORCID = \"0000-0002-4927-9204\")),\nperson(\"Hyungwon\", \"Choi\", , role = \"ctb\",\ncomment = c(ORCID = \"0000-0002-6687-3088\")),\nperson(\"Adam\", \"Claridge-Chang\", , role = \"ctb\",\ncomment = c(ORCID = \"0000-0002-4583-3650\")),\nperson(\"ACCLAB\", , role = c(\"cph\", \"fnd\"))\n)", "Description": "Data Analysis using Bootstrap-Coupled ESTimation.\nEstimation statistics is a simple framework that avoids the\npitfalls of significance testing. It uses familiar statistical\nconcepts: means, mean differences, and error bars. More\nimportantly, it focuses on the effect size of one's\nexperiment/intervention, as opposed to a false dichotomy\nengendered by P values. An estimation plot has two key\nfeatures: 1. It presents all datapoints as a swarmplot, which\norders each point to display the underlying distribution. 2.\nIt presents the effect size as a bootstrap 95% confidence\ninterval on a separate but aligned axes. Estimation plots are\nintroduced in Ho et al., Nature Methods 2019, 1548-7105.\n. The free-to-view PDF is\nlocated at\n.", "License": "Apache License (>= 2)", "URL": "https://github.com/ACCLAB/dabestr,\nhttps://acclab.github.io/dabestr/", "VignetteBuilder": "kableExtra, knitr", "Config/testthat/edition": "3", "Encoding": "UTF-8", "LazyData": "true", "Roxygen": "list(markdown = TRUE)", "RoxygenNote": "7.2.3", "Config/pak/sysreqs": "libicu-dev", "Repository": "https://acclab.r-universe.dev", "Date/Publication": "2025-09-15 08:41:13 UTC", "RemoteUrl": "https://github.com/acclab/dabestr", "RemoteRef": "HEAD", "RemoteSha": "7fb4eee98e4e0c3f05d7a471b12cd27f77ca922b", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-17 09:11:21 UTC", "User": "root" }, "Author": "Joses W. Ho [aut] (ORCID: ),\nKah Seng Lian [aut],\nAna Rosa Castillo [aut],\nZhuoyu Wang [aut],\nJun Yang Liao [aut],\nFelicia Low [aut],\nTayfun Tumkaya [aut] (ORCID: ),\nJonathan Anns [ctb] (ORCID: ),\nYishan Mai [cre, ctb] (ORCID: ),\nSangyu Xu [ctb] (ORCID: ),\nHyungwon Choi [ctb] (ORCID: ),\nAdam Claridge-Chang [ctb] (ORCID:\n),\nACCLAB [cph, fnd]", "Maintainer": "Yishan Mai ", "MD5sum": "81bd70121ffecd0025381eb2ed48fc22", "_user": "acclab", "_type": "src", "_file": "dabestr_2025.3.14.tar.gz", "_fileid": "77cd0074b8ad2bc2fe9dd4e892540c6bf28a4e336e9a75b3b4daf1f494b5cdca", "_filesize": 5227485, "_sha256": "77cd0074b8ad2bc2fe9dd4e892540c6bf28a4e336e9a75b3b4daf1f494b5cdca", "_created": "2026-05-17T09:11:21.000Z", "_published": "2026-06-02T17:05:47.908Z", "_distro": "noble", "_jobs": [ { "job": 79127677552, "time": 200, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "7041130560" }, { "job": 79127677744, "time": 195, "config": "linux-release-x86_64", "r": "4.6.0", 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