{ "_id": "6a101b47acfb0bcc41c86fce", "Package": "ehymet", "Title": "Methodologies for Functional Data Based on the Epigraph and\nHypograph Indices", "Version": "0.1.1", "Authors@R": "c(\nperson(given = \"Belen\", family = \"Pulido\", role = c(\"aut\", \"cre\"),\nemail = \"bpulidob4@gmail.com\",\ncomment = c(ORCID = \"0000-0003-2105-959X\")),\nperson(given = \"Jose Ignacio\", family = \"Diez\", role = c(\"ctr\"))\n)", "Description": "Implements methods for functional data analysis based on\nthe epigraph and hypograph indices. These methods transform\nfunctional datasets, whether in one or multiple dimensions,\ninto multivariate datasets. The transformation involves\napplying the epigraph, hypograph, and their modified versions\nto both the original curves and their first and second\nderivatives. The calculation of these indices is tailored to\nthe dimensionality of the functional dataset, with special\nconsiderations for dependencies between dimensions in\nmultidimensional cases. This approach extends traditional\nmultivariate data analysis techniques to the functional data\nsetting. A key application of this package is the EHyClus\nmethod, which enhances clustering analysis for functional data\nacross one or multiple dimensions using the epigraph and\nhypograph indices. See Pulido et al. (2023)\n and Pulido et al. (2024)\n.", "License": "MIT + file LICENSE", "Encoding": "UTF-8", "URL": "https://github.com/bpulidob/ehymet,\nhttps://bpulidob.github.io/ehymet/", "BugReports": "https://github.com/bpulidob/ehymet/issues", "Roxygen": "list(markdown = TRUE)", "RoxygenNote": "7.3.2", "Config/testthat/edition": "3", "VignetteBuilder": "knitr", "Repository": "https://bpulidob.r-universe.dev", "Date/Publication": "2025-07-02 14:24:24 UTC", "RemoteUrl": "https://github.com/bpulidob/ehymet", "RemoteRef": "HEAD", "RemoteSha": "041e4a9a1b618d589e88192f28cb90a3cd4b9666", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-14 09:12:19 UTC", "User": "root" }, "Author": "Belen Pulido [aut, cre] (ORCID:\n),\nJose Ignacio Diez [ctr]", "Maintainer": "Belen Pulido ", "MD5sum": "f158482632d5a1265fc62c70d1f13368", "_user": "bpulidob", "_type": "src", "_file": "ehymet_0.1.1.tar.gz", "_fileid": "dc16595eb3ad08764c0d8bf8dba690f541a7d0840228f422a8971943c2ac4860", "_filesize": 574702, "_sha256": "dc16595eb3ad08764c0d8bf8dba690f541a7d0840228f422a8971943c2ac4860", "_created": "2026-05-14T09:12:19.000Z", "_published": "2026-05-22T09:00:55.127Z", "_distro": "noble", "_jobs": [ { "job": 77348517593, "time": 137, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "6991095513" }, { "job": 77348517577, "time": 132, "config": "linux-release-x86_64", "r": "4.6.0", "check": "OK", "artifact": "6991092182" }, { "job": 77348517456, "time": 181, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "OK", "artifact": "6991096866" }, { "job": 77348517422, "time": 144, "config": "macos-release-arm64", "r": "4.6.0", "check": "OK", "artifact": "6991090253" }, { "job": 77348516927, "time": 188, "config": "source", "r": "4.6.0", "check": "OK", "artifact": "6991054093" }, { "job": 77348517069, "time": 103, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "7156734362" }, { "job": 77348517539, "time": 103, "config": "windows-devel", "r": "4.7.0", "check": "OK", "artifact": "6991084099" }, { "job": 77348517480, "time": 90, "config": "windows-oldrel", "r": "4.5.3", "check": "OK", "artifact": "6991081035" }, { "job": 77348517573, "time": 94, "config": "windows-release", "r": "4.6.0", "check": "OK", "artifact": "6991081622" } ], "_buildurl": "https://github.com/r-universe/bpulidob/actions/runs/25851787122", "_status": "success", "_host": "GitHub-Actions", "_upstream": "https://github.com/bpulidob/ehymet", "_commit": { "id": "041e4a9a1b618d589e88192f28cb90a3cd4b9666", "author": "unknown ", "committer": "unknown ", "message": "funding added\n", "time": 1751466264 }, "_maintainer": { "name": "Belen Pulido", "email": "bpulidob4@gmail.com", "login": "bpulidob", "uuid": 67836194, "orcid": "0000-0003-2105-959X" }, "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 4.1", "role": "Depends" }, { "package": "clusterCrit", "role": "Imports" }, { "package": "kernlab", "role": "Imports" }, { "package": "stats", "role": "Imports" }, { "package": "tf", "role": "Imports" }, { "package": "ggplot2", "role": "Suggests" }, { "package": "knitr", "role": "Suggests" }, { "package": "MASS", "role": "Suggests" }, { "package": "parallel", "role": "Suggests" }, { "package": "rmarkdown", "role": "Suggests" }, { "package": "testthat", "version": ">= 3.0.0", "role": "Suggests" }, { "package": "tidyr", "role": "Suggests" } ], "_owner": "bpulidob", "_selfowned": true, "_usedby": 0, "_updates": [ { "week": "2025-27", "n": 1 } ], "_tags": [], "_stars": 0, "_contributors": [ { "user": "bpulidob", "count": 40, "uuid": 67836194 }, { "user": "nachodieez", "count": 12, "uuid": 119790922 } ], "_userbio": { "uuid": 67836194, "type": "user", "name": "Belén Pulido", "description": "Postdoctoral researcher at IBiDat - UC3M-Santander Big Data Institute, with a remarkable background in Mathematics and Statistics." }, "_downloads": { "count": 190, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/ehymet" }, "_devurl": "https://github.com/bpulidob/ehymet", "_pkgdown": "https://bpulidob.github.io/ehymet/", "_searchresults": 10, "_rbuild": "4.6.0", "_assets": [ "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "extra/ehymet.html", "extra/NEWS.html", "extra/NEWS.txt", "extra/readme.html", "extra/readme.md", "manual.pdf" ], "_homeurl": "https://github.com/bpulidob/ehymet", "_realowner": "bpulidob", "_cranurl": true, "_releases": [ { "version": "0.1.0", "date": "2024-06-14" }, { "version": "0.1.1", "date": "2024-11-26" } ], "_exports": [ "clustering_validation", "clustInd_hierarch", "clustInd_kkmeans", "clustInd_kmeans", "clustInd_spc", "EHyClus", "EI", "generate_indices", "HI", "MEI", "MHI", "sim_model_ex1", "sim_model_ex2" ], "_help": [ { "page": "clustering_validation", "title": "Create a table containing four validation metrics for clustering: Purity, F-measure and Rand Index (RI) and Adjusted Rand Index (ARI). 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