{ "_id": "6a1017ccacfb0bcc41c851b1", "Package": "NU.Learning", "Version": "1.5", "Date": "2023-09-15", "Title": "Nonparametric and Unsupervised Learning from Cross-Sectional\nObservational Data", "Authors@R": "c(person(\"Bob\", \"Obenchain\", role = c(\"aut\", \"cre\"),\nemail = \"wizbob@att.net\"),\nperson(\"Stan\", \"Young\", role = c(\"ctb\"),\nemail = \"genetree@bellsouth.net\"))", "Author": "Bob Obenchain [aut, cre], Stan Young [ctb]", "Maintainer": "Bob Obenchain ", "Description": "Especially when cross-sectional data are observational,\neffects of treatment selection bias and confounding are best\nrevealed by using Nonparametric and Unsupervised methods to\n\"Design\" the analysis of the given data ...rather than the\ncollection of \"designed data\". Specifically, the \"effect-size\ndistribution\" that best quantifies a potentially causal\nrelationship between a numeric y-Outcome variable and either a\nbinary t-Treatment or continuous e-Exposure variable needs to\nconsist of BLOCKS of relatively well-matched experimental units\n(e.g. patients) that have the most similar X-confounder\ncharacteristics. Since our NU Learning approach will form\nBLOCKS by \"clustering\" experimental units in confounder\nX-space, the implicit statistical model for learning is One-Way\nANOVA. Within Block measures of effect-size are then either [a]\nLOCAL Treatment Differences (LTDs) between Within-Cluster\ny-Outcome Means (\"new\" minus \"control\") when treatment choice\nis Binary or else [b] LOCAL Rank Correlations (LRCs) when the\ne-Exposure variable is numeric with (hopefully many) more than\ntwo levels. An Instrumental Variable (IV) method is also\nprovided so that Local Average y-Outcomes (LAOs) within BLOCKS\nmay also contribute information for effect-size inferences when\nX-Covariates are assumed to influence Treatment choice or\nExposure level but otherwise have no direct effects on\ny-Outcomes. Finally, a \"Most-Like-Me\" function provides\nhistograms of effect-size distributions to aid Doctor-Patient\n(or Researcher-Society) communications about Heterogeneous\nOutcomes. Obenchain and Young (2013)\n; Obenchain, Young and Krstic\n(2019) .", "License": "GPL-2", "URL": "https://www.r-project.org, http://localcontrolstatistics.org", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-14 06:00:08 UTC", "User": "root" }, "Repository": "https://rlobenchain.r-universe.dev", "Date/Publication": "2023-10-01 01:39:42 UTC", "RemoteUrl": "https://github.com/cran/NU.Learning", "RemoteRef": "HEAD", "RemoteSha": "acedfecc3976daa7617683c4ddb8085ae6ac5d47", "MD5sum": "f8620995837a1fcc8454da5dc4aebc82", "_user": "rlobenchain", "_type": "src", "_file": "NU.Learning_1.5.tar.gz", "_fileid": "fc5b3aa5ad2e403f614e6942808fc8003b6f24397c77a091abc423649495d207", "_filesize": 2308805, "_sha256": "fc5b3aa5ad2e403f614e6942808fc8003b6f24397c77a091abc423649495d207", "_created": "2026-05-14T06:00:08.000Z", "_published": "2026-05-22T08:46:04.521Z", "_distro": "noble", "_jobs": [ { "job": 77346350776, "time": 117, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", 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