{ "_id": "6a104652acfb0bcc41c9eabb", "Package": "ordr", "Title": "A 'Tidyverse' Extension for Ordinations and Biplots", "Version": "0.2.0.0001", "Authors@R": "c(\nperson(\"Jason Cory\", \"Brunson\", email = \"cornelioid@gmail.com\",\nrole = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0003-3126-9494\")),\nperson(\"Emily\", \"Paul\", email = \"erpb.71@gmail.com\",\nrole = \"ctb\"),\nperson(\"John\", \"Gracey\", email = \"jbgracey6@gmail.com\",\nrole = \"aut\")\n)", "Description": "Ordination comprises several multivariate exploratory and\nexplanatory techniques with theoretical foundations in\ngeometric data analysis; see Podani (2000, ISBN:90-5782-067-6)\nfor techniques and applications and Le Roux & Rouanet (2005)\n for foundations. Greenacre (2010,\nISBN:978-84-923846) shows how the most established of these,\nincluding principal components analysis, correspondence\nanalysis, multidimensional scaling, factor analysis, and\ndiscriminant analysis, rely on eigen-decompositions or singular\nvalue decompositions of pre-processed numeric matrix data.\nThese decompositions give rise to a set of shared coordinates\nalong which the row and column elements can be measured. The\noverlay of their scatterplots on these axes, introduced by\nGabriel (1971) , is called a\nbiplot. 'ordr' provides inspection, extraction, manipulation,\nand visualization tools for several popular ordination classes\nsupported by a set of recovery methods. It is inspired by and\ndesigned to integrate into 'Tidyverse' workflows provided by\nWickham et al (2019) .", "License": "GPL-3", "Encoding": "UTF-8", "LazyData": "true", "URL": "https://github.com/corybrunson/ordr,\nhttps://corybrunson.github.io/ordr/", "BugReports": "https://github.com/corybrunson/ordr/issues", "RoxygenNote": "7.3.3", "Roxygen": "list(markdown = TRUE)", "Collate": "'aaa-.r' 'biplot-key.r' 'biplot.r' 'coord-scaffold.r' 'data.r'\n'dplyr-verbs.r' 'utils.r' 'ord-recoverers.r'\n'ord-augmentation.r' 'ord-conference.r' 'ord-tbl.r' 'fun-lda.r'\n'fun-lra.r' 'fun-wrap.r' 'geom-interpolation.r' 'geom-origin.r'\n'geom-utils.r' 'layer-utils.r' 'methods-base-eigen.r'\n'methods-base-svd.r' 'methods-mass-correspondence.r'\n'methods-mass-lda.r' 'methods-mass-mca.r' 'methods-ordr-lra.r'\n'methods-stats-cancor.r' 'methods-stats-cmds.r'\n'methods-stats-factanal.r' 'methods-stats-kmeans.r'\n'methods-stats-lm.r' 'methods-stats-prcomp.r'\n'methods-stats-princomp.r' 'ord-annotation.r' 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"recover_coord.correspondence", "recover_inertia.correspondence", "recover_rows.correspondence" ] }, { "page": "methods-eigen", "title": "Functionality for eigen-decompositions", "concept": [ "methods for eigen-decomposition-based techniques", "models from the base package" ], "topics": [ "as_tbl_ord.eigen", "as_tbl_ord.eigen_ord", "methods-eigen", "recover_aug_cols.eigen", "recover_aug_cols.eigen_ord", "recover_aug_coord.eigen", "recover_aug_coord.eigen_ord", "recover_aug_rows.eigen", "recover_aug_rows.eigen_ord", "recover_cols.eigen", "recover_cols.eigen_ord", "recover_conference.eigen", "recover_conference.eigen_ord", "recover_coord.eigen", "recover_coord.eigen_ord", "recover_inertia.eigen", "recover_inertia.eigen_ord", "recover_rows.eigen", "recover_rows.eigen_ord" ] }, { "page": "methods-factanal", "title": "Functionality for factor analysis ('factanal') objects", "concept": [ "methods for eigen-decomposition-based techniques", "models from the stats package" ], "topics": [ 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('lra') objects", "concept": [ "methods for singular value decomposition-based techniques" ], "topics": [ "as_tbl_ord.lra", "methods-lra", "recover_aug_cols.lra", "recover_aug_coord.lra", "recover_aug_rows.lra", "recover_cols.lra", "recover_conference.lra", "recover_coord.lra", "recover_inertia.lra", "recover_rows.lra" ] }, { "page": "methods-mca", "title": "Functionality for multiple correspondence analysis ('mca') objects", "concept": [ "methods for singular value decomposition-based techniques", "models from the MASS package" ], "topics": [ "as_tbl_ord.mca", "methods-mca", "recover_aug_cols.mca", "recover_aug_coord.mca", "recover_aug_rows.mca", "recover_cols.mca", "recover_conference.mca", "recover_coord.mca", "recover_inertia.mca", "recover_rows.mca", "recover_supp_rows.mca" ] }, { "page": "methods-prcomp", "title": "Functionality for principal components analysis ('prcomp') objects", "concept": [ "methods for singular value decomposition-based techniques", "models from the stats 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