{ "_id": "6a0f7052acfb0bcc41c5f48d", "Package": "seedCCA", "Type": "Package", "Title": "Seeded Canonical Correlation Analysis", "Version": "3.1", "Date": "2022-06-09", "Author": "Jae Keun Yoo, Bo-Young Kim", "Maintainer": "Jae Keun Yoo ", "Description": "Functions for dimension reduction through the seeded\ncanonical correlation analysis are provided. A classical\ncanonical correlation analysis (CCA) is one of useful\nstatistical methods in multivariate data analysis, but it is\nlimited in use due to the matrix inversion for large p small n\ndata. To overcome this, a seeded CCA has been proposed in Im,\nGang and Yoo (2015) \\doi{10.1002/cem.2691}. The seeded CCA is a\ntwo-step procedure. The sets of variables are initially reduced\nby successively projecting cov(X,Y) or cov(Y,X) onto cov(X) and\ncov(Y), respectively, without loss of information on canonical\ncorrelation analysis, following Cook, Li and Chiaromonte (2007)\n\\doi{10.1093/biomet/asm038} and Lee and Yoo (2014)\n\\doi{10.1111/anzs.12057}. Then, the canonical correlation is\nfinalized with the initially-reduced two sets of variables.", "License": "GPL (>= 2.0)", "LazyData": "yes", "Encoding": "UTF-8", "RoxygenNote": "6.0.1", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-21 07:27:50 UTC", "User": "root" }, "Repository": "https://cran.r-universe.dev", "Date/Publication": "2022-06-09 03:40:03 UTC", "RemoteUrl": "https://github.com/cran/seedCCA", "RemoteRef": "HEAD", "RemoteSha": "358df7aa68912a199731d55735294d30d12eb474", "MD5sum": "268aa83b7d9f4159e2e960da4d2f5a23", "_user": "cran", "_type": "src", "_file": "seedCCA_3.1.tar.gz", "_fileid": "9e58b2cf60513ef4ee947b633130915675678263263f1dd883fee1f3fa32292c", "_filesize": 319639, "_sha256": "9e58b2cf60513ef4ee947b633130915675678263263f1dd883fee1f3fa32292c", "_created": "2026-05-21T07:27:50.000Z", "_published": "2026-05-21T20:51:30.975Z", "_distro": "noble", "_jobs": [ { "job": 77266235009, "time": 133, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "NOTE", 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"tojson": true }, { "name": "nutrimouse", "title": "Nutrimouse dataset", "object": "nutrimouse", "class": [ "list" ], "fields": [], "table": false, "tojson": true } ], "_help": [ { "page": "coef.seedCCA", "title": "Coefficients of ordinary and partial least squares through iterative projections", "topics": [ "coef.seedCCA" ] }, { "page": "cookie", "title": "cookie dataset", "topics": [ "cookie" ] }, { "page": "covplot", "title": "scree-ploting cov(X, Y)", "topics": [ "covplot" ] }, { "page": "finalCCA", "title": "finalized CCA in seeded CCA", "topics": [ "finalCCA" ] }, { "page": "fitted.seedCCA", "title": "Fitted values of ordinary and partial least squares", "topics": [ "fitted.seedCCA" ] }, { "page": "iniCCA", "title": "Initialized CCA in seeded CCA", "topics": [ "iniCCA" ] }, { "page": "nutrimouse", "title": "Nutrimouse dataset", "topics": [ "nutrimouse" ] }, { "page": "plot.seedCCA", "title": "Plotting class \"seedCCA\" depending on the value of 'type'", "topics": [ "plot.seedCCA" ] }, { "page": "Pm", "title": "Projection of a seed matrix on to the column subspace of M with respect to 'Sx' inner-product", "topics": [ "Pm" ] }, { "page": "print.seedCCA", "title": "basic function for printing class \"seedCCA\"", "topics": [ "print.seedCCA" ] }, { "page": "seedCCA", "title": "Seeded Canonical correlation analysis", "topics": [ "seedCCA" ] }, { "page": "seeding", "title": "increments of iterative projections", "topics": [ "seeding" ] }, { "page": "seeding.auto.stop", "title": "increments of iterative projections with automatic stopping", "topics": [ "seeding.auto.stop" ] }, { "page": "seedols", "title": "Ordinary least squares", "topics": [ "seedols" ] }, { "page": "seedpls", "title": "Partial least squares through iterative projections", "topics": [ "seedpls" ] }, { "page": "selectu", "title": "Function that guides a selection of the terminating index when using seedCCA function", "topics": [ "selectu" ] } ], "_rundeps": [ "ash", "bitops", "CCA", "cli", "cluster", 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