{ "_id": "6a1fe206b401979e7343a227", "Package": "EHRtemporalVariability", "Type": "Package", "Title": "Delineating Temporal Dataset Shifts in Electronic Health Records", "Version": "1.2.2", "Date": "2026-04-27", "Encoding": "UTF-8", "Description": "Functions to delineate temporal dataset shifts in\nElectronic Health Records through the projection and\nvisualization of dissimilarities among data temporal batches.\nThis is done through the estimation of data statistical\ndistributions over time and their projection in non-parametric\nstatistical manifolds, uncovering the patterns of the data\nlatent temporal variability. 'EHRtemporalVariability' is\nparticularly suitable for multi-modal data and categorical\nvariables with a high number of values, common features of\nbiomedical data where traditional statistical process control\nor time-series methods may not be appropriate.\n'EHRtemporalVariability' allows you to explore and identify\ndataset shifts through visual analytics formats such as Data\nTemporal heatmaps and Information Geometric Temporal (IGT)\nplots. An additional 'EHRtemporalVariability' Shiny app can be\nused to load and explore the package results and even to allow\nthe use of these functions to those users non-experienced in R\ncoding. (Sáez et al. 2020) .", "License": "Apache License 2.0 | file LICENSE", "VignetteBuilder": "knitr", "NeedsCompilation": "no", "Authors@R": "c(\nperson(\"Carlos\", \"Sáez\", email = \"carsaesi@upv.es\", role = c(\"aut\", \"cre\")),\nperson(\"Alba\", \"Gutiérrez-Sacristán\", email = \"Alba_Gutierrez@hms.harvard.edu\", role = c(\"aut\")),\nperson(\"Isaac\", \"Kohane\", email = \"Isaac_Kohane@hms.harvard.edu\", role = c(\"aut\")),\nperson(\"Juan M\", \"García-Gómez\", email = \"juanmig@upv.es\", role = c(\"aut\")),\nperson(\"Paul\", \"Avillach\", email = \"Paul_Avillach@hms.harvard.edu\", role = c(\"aut\")),\nperson(\"Biomedical Data Science Lab, Universitat Politècnica de València (Spain)\", role = c(\"cph\")),\nperson(\"Department of Biomedical Informatics, Harvard Medical School\", role = c(\"cph\"))\n)", "Maintainer": "Carlos Sáez ", "RoxygenNote": "7.3.3", "URL": "https://github.com/hms-dbmi/EHRtemporalVariability", "Config/pak/sysreqs": "cmake make libicu-dev libuv1-dev libssl-dev", "Repository": "https://hms-dbmi.r-universe.dev", "Date/Publication": "2026-04-28 07:41:48 UTC", "RemoteUrl": "https://github.com/hms-dbmi/ehrtemporalvariability", "RemoteRef": "HEAD", "RemoteSha": "038e6c5b9ed8fb88f46cf0be2519f0ed2a823adf", "Packaged": { "Date": "2026-06-03 08:09:06 UTC", "User": "root" }, "Author": "Carlos Sáez [aut, cre],\nAlba Gutiérrez-Sacristán [aut],\nIsaac Kohane [aut],\nJuan M García-Gómez [aut],\nPaul Avillach [aut],\nBiomedical Data Science Lab, Universitat Politècnica de València\n(Spain) [cph],\nDepartment of Biomedical Informatics, Harvard Medical School [cph]", "MD5sum": "ede06a732dbea1035c1ec079a834b887", "_user": "hms-dbmi", "_type": "src", "_file": "EHRtemporalVariability_1.2.2.tar.gz", "_fileid": "b30d55ce4b4dddb6d6231870c01c158c74db54d59b127058bf3e4867fd3b43ac", "_filesize": 2402870, "_sha256": "b30d55ce4b4dddb6d6231870c01c158c74db54d59b127058bf3e4867fd3b43ac", "_created": "2026-06-03T08:09:06.000Z", "_published": "2026-06-03T08:12:54.080Z", "_distro": "noble", "_jobs": [ { "job": 79249821139, "time": 162, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "7379125634" }, { "job": 79249821052, "time": 165, "config": "linux-release-x86_64", "r": "4.6.0", "check": "OK", "artifact": "7379126386" }, { "job": 79249821024, "time": 169, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "OK", "artifact": "7379115829" }, { "job": 79249821084, "time": 173, "config": "macos-release-arm64", "r": "4.6.0", "check": "OK", "artifact": "7379120249" }, { "job": 79249248692, "time": 213, "config": "source", "r": "4.6.0", "check": "OK", "artifact": "7379066710" }, { "job": 79249821065, "time": 128, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "7379112571" }, { "job": 79249821016, "time": 83, "config": "windows-devel", "r": "4.7.0", "check": "OK", "artifact": "7379096562" }, { "job": 79249821069, "time": 90, "config": "windows-oldrel", "r": "4.5.3", "check": "OK", "artifact": "7379099095" }, { "job": 79249821049, "time": 95, "config": "windows-release", "r": "4.6.0", "check": "OK", "artifact": "7379100974" } ], "_buildurl": "https://github.com/r-universe/hms-dbmi/actions/runs/26871946341", "_status": "success", "_host": "GitHub-Actions", "_upstream": "https://github.com/hms-dbmi/ehrtemporalvariability", "_commit": { "id": "038e6c5b9ed8fb88f46cf0be2519f0ed2a823adf", "author": "carsaesi ", "committer": "carsaesi ", "message": "Improved vignette compliance with CRAN policies. Added wordlist.\n", "time": 1777362108 }, "_maintainer": { "name": "Carlos Sáez", "email": "carsaesi@upv.es", "login": "carsaesi", "description": "Associate Professor, Biomedical Data Science Researcher at Universitat Politècnica de València, Spain", "uuid": 38731725 }, "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 4.3.0", "role": "Depends" }, { "package": "dplyr", "role": "Depends" }, { "package": "plotly", "role": "Imports" }, { "package": "zoo", "role": "Imports" }, { "package": "xts", "role": "Imports" }, { "package": "lubridate", "role": "Imports" }, { "package": "RColorBrewer", "role": "Imports" }, { "package": "viridis", "role": "Imports" }, { "package": "scales", "role": "Imports" }, { "package": "methods", "role": "Imports" }, { "package": "MASS", "role": "Imports" }, { "package": "knitr", "role": "Suggests" }, { "package": "rmarkdown", "role": "Suggests" }, { "package": "devtools", "role": "Suggests" }, { "package": "dbscan", "role": "Suggests" }, { "package": "webshot", "role": "Suggests" }, { "package": "httr", "role": "Suggests" } ], "_owner": "hms-dbmi", "_selfowned": true, "_usedby": 0, "_updates": [ { "week": "2026-18", "n": 4 } ], "_tags": [], "_topics": [ "biomedical-data-science", "biomedical-informatics", "data-quality", "data-quality-monitoring", "dataset-shifts", "electronic-health-records", "time", "variability", "visualization" ], "_stars": 17, "_contributors": [ { "user": "carsaesi", "count": 43, "uuid": 38731725 } ], "_userbio": { "uuid": 5939932, "type": "organization", "name": "Harvard Medical School - Department of Biomedical Informatics" }, "_downloads": { "count": 455, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/EHRtemporalVariability" }, "_mentions": 1, "_devurl": "https://github.com/hms-dbmi/ehrtemporalvariability", "_rbuild": "4.6.0", "_assets": [ "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "extra/EHRtemporalVariability.html", "extra/NEWS.html", "extra/NEWS.txt", "extra/readme.html", "extra/readme.md", "manual.pdf" ], "_homeurl": "https://github.com/hms-dbmi/ehrtemporalvariability", "_realowner": "hms-dbmi", "_cranurl": true, "_releases": [ { "version": "1.0", "date": "2019-03-14" }, { "version": "1.0.1", "date": "2019-06-11" }, { "version": "1.0.2", "date": "2019-09-26" }, { "version": "1.0.3", "date": "2020-03-26" }, { "version": "1.0.4", "date": "2020-03-28" }, { "version": "1.1.0", "date": "2020-05-21" }, { "version": "1.1.1", "date": "2020-05-25" }, { "version": "1.1.2", "date": "2020-10-08" }, { "version": "1.1.2.1", "date": "2020-10-09" }, { "version": "1.1.4", "date": "2021-05-31" }, { "version": "1.2.0", "date": "2023-12-17" }, { "version": "1.2.1", "date": "2024-01-26" }, { "version": "1.2.2", "date": "2026-04-28" } ], "_exports": [ "estimateDataTemporalMap", "estimateIGTProjection", "estimateIGTTrajectory", "formatDate", "icd9toPheWAS", "plotDataTemporalMap", "plotIGTProjection", "trimDataTemporalMap" ], "_help": [ { "page": "DataTemporalMap-class", "title": "Class DataTemporalMap", "topics": [ "DataTemporalMap", "DataTemporalMap,DataTemporalMap-class", "DataTemporalMap-class" ] }, { "page": "estimateDataTemporalMap-methods", "title": "Estimates 'DataTemporalMap' objects from raw data", "topics": [ "estimateDataTemporalMap" ] }, { "page": "estimateIGTProjection-methods", "title": "Estimates an Information Geometric Temporal plot projection", "topics": [ "estimateIGTProjection", "estimateIGTProjection,DataTemporalMap-method", "estimateIGTProjection,IGTProjection-method" ] }, { "page": "estimateIGTTrajectory-methods", "title": "Estimates a trajectory of the information temporal evolution in a IGT projection by fitting a cubic smoothing spline", "topics": [ "estimateIGTTrajectory" ] }, { "page": "formatDate", "title": "Function to transform dates into \"Date\" R format", "topics": [ "formatDate" ] }, { "page": "icd9toPheWAS", "title": "Function to transform ICD9-CM codification into PheWAS code", "topics": [ "icd9toPheWAS" ] }, { "page": "IGTProjection-class", "title": "Class IGTProjection", "topics": [ "IGTProjection", "IGTProjection,IGTProjection-class", "IGTProjection-class" ] }, { "page": "plotDataTemporalMap-methods", "title": "Data Temporal heatmap", "topics": [ "plotDataTemporalMap", "plotDataTemporalMap,DataTemporalMap-method" ] }, { "page": "plotIGTProjection-methods", "title": "Information Geometric Temporal plot", "topics": [ "plotIGTProjection", "plotIGTProjection,IGTProjection-method" ] }, { "page": "trimDataTemporalMap-methods", "title": "Trims a 'DataTemporalMap'", "topics": [ "trimDataTemporalMap", "trimDataTemporalMap,DataTemporalMap-method" ] } ], "_readme": "https://github.com/hms-dbmi/ehrtemporalvariability/raw/HEAD/README.md", "_rundeps": [ "askpass", "base64enc", "bslib", "cachem", "cli", "cpp11", "crosstalk", "curl", "data.table", "digest", "dplyr", "evaluate", "farver", "fastmap", "fontawesome", "fs", "generics", "ggplot2", "glue", "gridExtra", "gtable", "highr", "htmltools", "htmlwidgets", "httr", "isoband", "jquerylib", "jsonlite", "knitr", "labeling", "later", "lattice", "lazyeval", "lifecycle", "lubridate", "magrittr", "MASS", "memoise", "mime", "openssl", "otel", "pillar", "pkgconfig", "plotly", "promises", "purrr", "R6", "rappdirs", "RColorBrewer", "Rcpp", "rlang", "rmarkdown", "S7", "sass", "scales", "stringi", "stringr", "sys", "tibble", "tidyr", "tidyselect", "timechange", "tinytex", "utf8", "vctrs", "viridis", "viridisLite", "withr", "xfun", "xts", "yaml", "zoo" ], "_vignettes": [ { "source": "EHRtemporalVariability.Rmd", "filename": "EHRtemporalVariability.html", "title": "EHRtemporalVariability: Delineating temporal dataset shifts in Electronic Health Records", "engine": "knitr::rmarkdown", "headings": [ "Introduction", "Background", "Installation", "S4 objects", "DataTemporalMap", "IGTProjection", "Data pre-processing", "Load the CSV input file", "Transform the date column in 'Date' R format", "Transform the ICD9-CM into PheWAS codes", "Data analysis", "Estimate data temporal map", "Trim a DataTemporalMap object", "Estimate IGT projections", "Data visualization", "Plot data temporal maps", "Heatmap plot", "Time series plot", "Plot IGT projections", "Visualize temporal trajectory", "Export data for the Shiny app dashboard", "Interpretation of temporal changes in IGT projections", "Types of temporal changes", "Example", "Summary of EHRtemporalVariability available functions", "Bibliography" ], "created": "2019-03-01 15:39:24", "modified": "2026-04-28 07:41:48", "commits": 17 } ], "_score": 4.929418925714293, "_indexed": true, "_nocasepkg": "ehrtemporalvariability", "_universes": [ "hms-dbmi", "carsaesi" ], "_binaries": [ { "r": "4.7.0", "os": "linux", "version": "1.2.2", "date": "2026-06-03T08:11:39.000Z", "distro": "noble", "commit": "038e6c5b9ed8fb88f46cf0be2519f0ed2a823adf", "fileid": "2222848b219d03d25a697ea429fa20150b2255256e5f46e4e83c6bd555870b86", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/hms-dbmi/actions/runs/26871946341" }, { "r": "4.6.0", "os": "linux", "version": "1.2.2", "date": "2026-06-03T08:11:39.000Z", "distro": "noble", "commit": "038e6c5b9ed8fb88f46cf0be2519f0ed2a823adf", "fileid": "d474af66299ee6cdc3b59b67edc730f23d976f5cfdba10f0d518c293ede37643", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/hms-dbmi/actions/runs/26871946341" }, { "r": "4.5.3", "os": "mac", "version": "1.2.2", "date": "2026-06-03T08:11:20.000Z", "commit": "038e6c5b9ed8fb88f46cf0be2519f0ed2a823adf", "fileid": "c553289ab7bd09f20a7501fdbf6e8c9bc4da9aa13565f09c2878f020ed39f49a", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/hms-dbmi/actions/runs/26871946341" }, { "r": "4.6.0", "os": "mac", "version": "1.2.2", "date": "2026-06-03T08:11:27.000Z", "commit": "038e6c5b9ed8fb88f46cf0be2519f0ed2a823adf", "fileid": "25bd1ab0a837990a9d4265e19787e40fb31d31e2c1cc4c5f73981d2539cf795b", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/hms-dbmi/actions/runs/26871946341" }, { "r": "4.6.0", "os": "wasm", "version": "1.2.2", "date": "2026-06-03T08:11:32.000Z", "commit": "038e6c5b9ed8fb88f46cf0be2519f0ed2a823adf", "fileid": "3ef093a550717ccdd5067e68ab3a4d5f24b3bcc68d34f5ad16d84ee83dc32970", "status": "success", "buildurl": "https://github.com/r-universe/hms-dbmi/actions/runs/26871946341" }, { "r": "4.7.0", "os": "win", "version": "1.2.2", "date": "2026-06-03T08:10:24.000Z", "commit": "038e6c5b9ed8fb88f46cf0be2519f0ed2a823adf", "fileid": "b4df485aa4f2df604cef6100c03ae3c28119d5b17fd298ff67394daf33d57da9", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/hms-dbmi/actions/runs/26871946341" }, { "r": "4.5.3", "os": "win", "version": "1.2.2", "date": "2026-06-03T08:10:20.000Z", "commit": "038e6c5b9ed8fb88f46cf0be2519f0ed2a823adf", "fileid": "f34cc71495539c88b8111b877141edb18d3049c2bf89539d6588f646cc4d6f65", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/hms-dbmi/actions/runs/26871946341" }, { "r": "4.6.0", "os": "win", "version": "1.2.2", "date": "2026-06-03T08:10:23.000Z", "commit": "038e6c5b9ed8fb88f46cf0be2519f0ed2a823adf", "fileid": "788bfb153713253c01a327b7d81607a888724efab50ee5193c7245fb9a27dd0a", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/hms-dbmi/actions/runs/26871946341" } ] }