{ "_id": "6a23b9da530b9bc726bd8935", "Package": "jackstraw", "Type": "Package", "Title": "Statistical Inference for Unsupervised Learning", "Version": "1.3.21", "Authors@R": "c(\nperson(given = c(\"Neo\", \"Christopher\"),\nfamily = \"Chung\",\nrole = c(\"aut\", \"cre\"),\nemail = \"nchchung@gmail.com\",\ncomment = c(ORCID = \"0000-0001-6798-8867\")),\nperson(given = c(\"John\", \"D.\"),\nfamily = \"Storey\",\nrole = \"aut\",\nemail = \"jstorey@princeton.edu\",\ncomment = c(ORCID = \"0000-0001-5992-402X\")),\nperson(given = \"Wei\",\nfamily = \"Hao\",\nrole = \"aut\",\nemail = \"whao@princeton.edu\"),\nperson(given = \"Alejandro\",\nfamily = \"Ochoa\",\nrole = \"aut\",\nemail = \"alejandro.ochoa@duke.edu\",\ncomment = c(ORCID = \"0000-0003-4928-3403\"))\n)", "Description": "Test for association between the observed data and their\nestimated latent variables. The jackstraw package provides a\nresampling strategy and testing scheme to estimate statistical\nsignificance of association between the observed data and their\nlatent variables. Depending on the data type and the analysis\naim, the latent variables may be estimated by principal\ncomponent analysis (PCA), factor analysis (FA), K-means\nclustering, and related unsupervised learning algorithms. The\njackstraw methods learn over-fitting characteristics inherent\nin this circular analysis, where the observed data are used to\nestimate the latent variables and used again to test against\nthat estimated latent variables. When latent variables are\nestimated by PCA, the jackstraw enables statistical testing for\nassociation between observed variables and latent variables, as\nestimated by low-dimensional principal components (PCs). This\nessentially leads to identifying variables that are\nsignificantly associated with PCs. Similarly, unsupervised\nclustering, such as K-means clustering, partition around\nmedoids (PAM), and others, finds coherent groups in\nhigh-dimensional data. The jackstraw estimates statistical\nsignificance of cluster membership, by testing association\nbetween data and cluster centers. Clustering membership can be\nimproved by using the resulting jackstraw p-values and\nposterior inclusion probabilities (PIPs), with an application\nto unsupervised evaluation of cell identities in single cell\nRNA-seq (scRNA-seq).", "LazyData": "true", "biocViews": "", "License": "GPL-2", "Encoding": "UTF-8", "Roxygen": "list(markdown = TRUE)", "RoxygenNote": "7.3.3", "Config/testthat/edition": "3", "Config/pak/sysreqs": "libgmp3-dev libx11-dev", "Repository": "https://ncchung.r-universe.dev", "Date/Publication": "2026-04-07 18:13:45 UTC", "RemoteUrl": "https://github.com/ncchung/jackstraw", "RemoteRef": "HEAD", "RemoteSha": "26f38cdfe0de86509737809c2d4332293428d63c", "NeedsCompilation": "no", "Packaged": { "Date": "2026-06-06 05:45:45 UTC", "User": "root" }, "Author": "Neo Christopher Chung [aut, cre] (ORCID:\n),\nJohn D. Storey [aut] (ORCID: ),\nWei Hao [aut],\nAlejandro Ochoa [aut] (ORCID: )", "Maintainer": "Neo Christopher Chung ", "MD5sum": "c9449af3af2e43f338bd53a77cd0f5e4", "_user": "ncchung", "_type": "src", "_file": "jackstraw_1.3.21.tar.gz", "_fileid": "34dfc6fed2f31fd99715d4cd2c7f1b4c72a32bf51c4b6215515ac8a4ab97b68f", "_filesize": 503148, "_sha256": "34dfc6fed2f31fd99715d4cd2c7f1b4c72a32bf51c4b6215515ac8a4ab97b68f", "_created": "2026-06-06T05:45:45.000Z", "_published": "2026-06-06T06:10:34.505Z", "_distro": "noble", "_jobs": [ { "job": 79855056645, "time": 1458, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "7451843207" }, { "job": 79855056647, "time": 1455, "config": "linux-release-x86_64", "r": "4.6.0", "check": "OK", "artifact": "7451842985" }, { "job": 79855056652, "time": 87, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "OK", "artifact": "7451671031" }, { "job": 79855056649, "time": 77, "config": "macos-release-arm64", "r": "4.6.0", "check": "OK", 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