{"id":18430172,"url":"https://github.com/friendly/candisc","last_synced_at":"2025-04-07T17:33:32.759Z","repository":{"id":56934538,"uuid":"103676717","full_name":"friendly/candisc","owner":"friendly","description":"Visualizing Generalized Canonical Discriminant and Canonical Correlation Analysis","archived":false,"fork":false,"pushed_at":"2024-05-07T02:59:52.000Z","size":31579,"stargazers_count":15,"open_issues_count":1,"forks_count":4,"subscribers_count":6,"default_branch":"master","last_synced_at":"2024-09-21T13:20:15.924Z","etag":null,"topics":["dimension-reduction","multivariate-linear-models","visualization"],"latest_commit_sha":null,"homepage":"https://friendly.github.io/candisc/","language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/friendly.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2017-09-15T16:10:12.000Z","updated_at":"2024-05-07T02:59:56.000Z","dependencies_parsed_at":"2024-06-20T00:23:15.014Z","dependency_job_id":null,"html_url":"https://github.com/friendly/candisc","commit_stats":{"total_commits":231,"total_committers":4,"mean_commits":57.75,"dds":"0.47619047619047616","last_synced_commit":"e0bbf1506a5db7b00d00ae5f475035bc0c08302a"},"previous_names":[],"tags_count":3,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/friendly%2Fcandisc","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/friendly%2Fcandisc/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/friendly%2Fcandisc/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/friendly%2Fcandisc/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/friendly","download_url":"https://codeload.github.com/friendly/candisc/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247698054,"owners_count":20981294,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["dimension-reduction","multivariate-linear-models","visualization"],"created_at":"2024-11-06T05:19:47.834Z","updated_at":"2025-04-07T17:33:27.750Z","avatar_url":"https://github.com/friendly.png","language":"R","readme":"\u003c!-- badges: start --\u003e\n\n[![Lifecycle: stable](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://lifecycle.r-lib.org/articles/stages.html#stable) \n[![CRAN_Status_Badge](http://www.r-pkg.org/badges/version/candisc)](https://cran.r-project.org/package=candisc)\n[![](https://cranlogs.r-pkg.org/badges/grand-total/candisc)](https://cran.r-project.org/package=candisc)\n[![](https://img.shields.io/badge/documentation-blue)](https://friendly.github.io/candisc)\n[![](https://friendly.r-universe.dev/badges/candisc)](https://friendly.r-universe.dev) \n\n\u003c!-- badges: end --\u003e\n\n\n# candisc  \u003cimg src=\"man/figures/logo.png\" align=\"right\" height=\"160px\" /\u003e\n**Visualizing Generalized Canonical Discriminant and Canonical Correlation Analysis**\n\nVersion 0.7.0\n\nThis package includes functions for computing and visualizing \ngeneralized canonical discriminant analyses \nand canonical correlation analysis\nfor a multivariate linear model.  The goal is to provide ways of visualizing\nsuch models in a low-dimensional space corresponding to dimensions\n(linear combinations of the response variables) of maximal relationship\nto the predictor variables. \n\nTraditional canonical discriminant analysis is restricted to a one-way MANOVA\ndesign and is equivalent to canonical correlation analysis between a set of quantitative\nresponse variables and a set of dummy variables coded from the factor variable.\nThe `candisc` package generalizes this to multi-way MANOVA designs\nfor all terms in a multivariate linear model (i.e., an `mlm` object),\ncomputing canonical scores and vectors for each term (giving a `\"candiscList\"` object).\n\nThe graphic functions are designed to provide low-rank (1D, 2D, 3D) visualizations of\nterms in a `mlm` via the `plot.candisc` method, \nand the HE plot `heplot.candisc()` and `heplot3d.candisc()`\nmethods.\nFor `mlm`s with more than a few response variables, these methods often provide a \nmuch simpler interpretation of the nature of effects in canonical space than\nheplots for pairs of responses or an HE plot matrix of all responses in variable space.\n\nAnalogously, a multivariate linear (regression) model with quantitative predictors can also be\nrepresented in a reduced-rank space by means of a canonical correlation\ntransformation of the Y and X variables to uncorrelated canonical variates,\nYcan and Xcan.  Computation for this analysis is provided by `cancor`\nand related methods.  Visualization of these results in canonical space\nare provided by the `plot.cancor()`,  `heplot.cancor()` \nand `heplot3d.cancor()` methods.\n\nThese relations among response variables in linear models can also be\nuseful for \"effect ordering\"\n(Friendly \u0026 Kwan (2003)\nfor *variables* in other multivariate data displays to make the\ndisplayed relationships more coherent.  The function `varOrder()`\nimplements a collection of these methods.\n\n\n## Installation\n\n|                     |                                               |\n|---------------------|-----------------------------------------------|\n| CRAN version        | `install.packages(\"candisc\")`                 |\n| Development version | `remotes::install_github(\"friendly/candisc\")` |\n\nOr, install from r-universe\n\n```r\ninstall.packages('candisc', repos = c('https://friendly.r-universe.dev')\n```\n\n## Vignettes\n\n* A new vignette, `vignette(\"diabetes\", package=\"candisc\")`,\nillustrates some of these methods.\n\n* A more comprehensive collection of examples is contained in the vignette for the `heplots` package,\n`browseVignettes(package = \"heplots\")`.\n\n\n ","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffriendly%2Fcandisc","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ffriendly%2Fcandisc","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffriendly%2Fcandisc/lists"}