{ "_id": "6a23ba60530b9bc726bd8d30", "Type": "Package", "Package": "GeDS", "Title": "Geometrically Designed Spline Regression", "Version": "0.3.4", "Date": "2025-09-05", "Authors@R": "c(\nperson(c(\"Dimitrina\", \"S.\"), \"Dimitrova\", , \"D.Dimitrova@citystgeorges.ac.uk\", role = \"aut\"),\nperson(c(\"Vladimir\", \"K.\"), \"Kaishev\", , \"Vladimir.Kaishev.1@citystgeorges.ac.uk\", role = \"aut\"),\nperson(\"Andrea\", \"Lattuada\", , \"Andrea.Lattuada@hotmail.com\", role = \"aut\"),\nperson(c(\"Emilio\", \"L.\"), \"Sáenz Guillén\", , \"Emilio.Saenz-Guillen@citystgeorges.ac.uk\", role = c(\"aut\", \"cre\")),\nperson(c(\"Richard\", \"J.\"), \"Verrall\", , \"R.J.Verrall@citystgeorges.ac.uk\", role = \"aut\")\n)", "Maintainer": "Emilio L. Sáenz Guillén\n", "Description": "Spline regression, generalized additive models and\ncomponent-wise gradient boosting utilizing geometrically\ndesigned (GeD) splines. GeDS regression is a non-parametric\nmethod inspired by geometric principles, for fitting spline\nregression models with variable knots in one or two independent\nvariables. It efficiently estimates the number of knots and\ntheir positions, as well as the spline order, assuming the\nresponse variable follows a distribution from the exponential\nfamily. GeDS models integrate the broader category of\ngeneralized (non-)linear models, offering a flexible approach\nto model complex relationships. A description of the method can\nbe found in Kaishev et al. (2016)\n and Dimitrova et al. (2023)\n. Further extending its\ncapabilities, GeDS's implementation includes generalized\nadditive models (GAM) and functional gradient boosting (FGB),\nenabling versatile multivariate predictor modeling, as\ndiscussed in the forthcoming work of Dimitrova et al. (2025).", "License": "GPL-3", "URL": "https://github.com/emilioluissaenzguillen/GeDS", "BugReports": "https://github.com/emilioluissaenzguillen/GeDS/issues", "VignetteBuilder": "knitr, R.rsp", "Config/testthat/edition": "3", "Encoding": "UTF-8", "LazyData": "TRUE", "RoxygenNote": "7.3.3", "Repository": "https://emilioluissaenzguillen.r-universe.dev", "Date/Publication": "2026-05-07 17:10:44 UTC", "RemoteUrl": "https://github.com/emilioluissaenzguillen/geds", "RemoteRef": "HEAD", "RemoteSha": "dd12c06c044d53aba45afe6c7a4ae2f29e92205b", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-06-06 05:54:58 UTC", "User": "root" }, "Author": "Dimitrina S. Dimitrova [aut],\nVladimir K. Kaishev [aut],\nAndrea Lattuada [aut],\nEmilio L. Sáenz Guillén [aut, cre],\nRichard J. Verrall [aut]", "MD5sum": "f6620921736559364e9e90d68a7a54fc", "_user": "emilioluissaenzguillen", "_type": "src", "_file": "GeDS_0.3.4.tar.gz", "_fileid": "01a6d1acb6c8a65b4946ab37736f59d42840a480faebd21b786ea5c10493f131", "_filesize": 23795011, "_sha256": "01a6d1acb6c8a65b4946ab37736f59d42840a480faebd21b786ea5c10493f131", "_created": "2026-06-06T05:54:58.000Z", "_published": "2026-06-06T06:12:48.119Z", "_distro": "noble", "_jobs": [ { "job": 79855642517, "time": 997, "config": "linux-devel-arm64", "r": "4.7.0", "check": "OK", "artifact": "7451859095" }, { "job": 79855642515, "time": 247, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "ERROR", "artifact": "7451767470" }, { "job": 79855642514, "time": 993, "config": "linux-release-arm64", "r": "4.6.0", "check": "OK", "artifact": "7451858239" }, { "job": 79855642509, "time": 240, "config": "linux-release-x86_64", "r": "4.6.0", "check": "ERROR", "artifact": "7451766585" }, { "job": 79855642527, "time": 274, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "OK", "artifact": "7451766778" }, { "job": 79855642512, "time": 450, "config": "macos-oldrel-x86_64", "r": "4.5.3", "check": "OK", "artifact": "7451790919" }, { "job": 79855642528, "time": 338, "config": "macos-release-arm64", "r": "4.6.0", "check": "OK", "artifact": "7451774991" }, { "job": 79855642523, "time": 553, "config": "macos-release-x86_64", "r": "4.6.0", "check": "OK", "artifact": "7451802310" }, { "job": 79855312721, "time": 327, "config": "source", "r": "4.6.0", "check": "OK", "artifact": "7451735697" }, { "job": 79855642492, "time": 124, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "7451751546" }, { "job": 79855642543, "time": 212, "config": "windows-devel", "r": "4.7.0", "check": "OK", "artifact": "7451762852" }, { "job": 79855642538, "time": 209, "config": "windows-oldrel", "r": "4.5.3", "check": "OK", "artifact": "7451762397" }, { "job": 79855642525, "time": 223, "config": "windows-release", "r": "4.6.0", "check": "OK", "artifact": "7451764084" } ], "_buildurl": "https://github.com/r-universe/emilioluissaenzguillen/actions/runs/27054217010", "_status": "success", "_host": "GitHub-Actions", "_upstream": "https://github.com/emilioluissaenzguillen/geds", "_commit": { "id": "dd12c06c044d53aba45afe6c7a4ae2f29e92205b", "author": "Saenz Guillen ", "committer": "Saenz Guillen ", "message": "Base-learners selected only FGB-GeDS\n", "time": 1778173844 }, "_maintainer": { "name": "Emilio L. Sáenz Guillén", "email": "emilio.saenz-guillen@citystgeorges.ac.uk" }, "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 4.4.0", "role": "Depends" }, { "package": "Rcpp", "role": "LinkingTo" }, { "package": "doFuture", "role": "Imports" }, { "package": "doParallel", "role": "Imports" }, { "package": "doRNG", "role": "Imports" }, { "package": "foreach", "role": "Imports" }, { "package": "future", "role": "Imports" }, { "package": "graphics", "role": "Imports" }, { "package": "grDevices", "role": "Imports" }, { "package": "MASS", "role": "Imports" }, { "package": "Matrix", "role": "Imports" }, { "package": "mboost", "role": "Imports" }, { "package": "parallel", "role": "Imports" }, { "package": "plot3D", "role": "Imports" }, { "package": "Rcpp", "role": "Imports" }, { "package": "splines", "role": "Imports" }, { "package": "stats", "role": "Imports" }, { "package": "utils", "role": "Imports" }, { "package": "AmesHousing", "role": "Suggests" }, { "package": "knitr", "role": "Suggests" }, { "package": "R.rsp", "role": "Suggests" }, { "package": "rmarkdown", "role": "Suggests" }, { "package": "rpart", "role": "Suggests" }, { "package": "testthat", "version": ">= 3.0.0", "role": "Suggests" }, { "package": "TH.data", "role": "Suggests" } ], "_owner": "emilioluissaenzguillen", "_selfowned": true, "_usedby": 0, "_updates": [ { "week": "2025-24", "n": 1 }, { "week": "2025-25", "n": 1 }, { "week": "2025-26", "n": 5 }, { "week": "2025-27", "n": 1 }, { "week": "2025-28", "n": 1 }, { "week": "2025-30", "n": 1 }, { "week": "2025-31", "n": 1 }, { "week": "2025-34", "n": 1 }, { "week": "2025-35", "n": 1 }, { "week": "2025-37", "n": 1 }, { "week": "2025-38", "n": 1 }, { "week": "2025-42", "n": 1 }, { "week": "2025-48", "n": 1 }, { "week": "2026-12", "n": 1 }, { "week": "2026-13", "n": 1 }, { "week": "2026-19", "n": 1 } ], "_tags": [], "_stars": 2, "_contributors": [ { "user": "emilioluissaenzguillen", "count": 69, "uuid": 116019934 } ], "_userbio": { "uuid": 116019934, "type": "user", "name": "Emilio Luis Sáenz Guillén", "description": "Doctoral researcher in Actuarial Science at Bayes Business School." }, "_downloads": { "count": 596, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/GeDS" }, "_devurl": "https://github.com/emilioluissaenzguillen/geds", "_searchresults": 30, "_topics": [ "cpp" ], "_rbuild": "4.6.0", "_assets": [ "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "extra/GeDS.html", "extra/readme.html", "extra/readme.md", "manual.pdf" ], "_homeurl": "https://github.com/emilioluissaenzguillen/geds", "_realowner": "emilioluissaenzguillen", "_cranurl": true, "_releases": [ { "version": "0.1.2", "date": "2017-11-08" }, { "version": "0.1.3", "date": "2017-12-19" }, { "version": "0.1.4", "date": "2023-12-06" }, { "version": "0.2.0", "date": "2024-03-24" }, { "version": "0.2.1", "date": "2024-04-11" }, { "version": "0.2.2", "date": "2024-04-24" }, { "version": "0.2.3", "date": "2024-07-19" }, { "version": "0.2.4", "date": "2024-09-12" }, { "version": "0.2.5", "date": "2025-01-21" }, { "version": "0.2.6", "date": "2025-02-10" }, { "version": "0.2.7", "date": "2025-03-11" }, { "version": "0.2.8", "date": "2025-03-14" }, { "version": "0.2.9", "date": "2025-03-28" }, { "version": "0.3.0", "date": "2025-04-16" }, { "version": "0.3.1", "date": "2025-04-22" }, { "version": "0.3.2", "date": "2025-06-10" }, { "version": "0.3.3", "date": "2025-06-30" } ], "_exports": [ "BivariateFitter", "bl_imp", "crossv_GeDS", "Derive", "GenBivariateFitter", "GenUnivariateFitter", "GGeDS", "Integrate", "IRLSfit", "N.boost.iter", "NGeDS", "NGeDSboost", "NGeDSgam", "PPolyRep", "SplineReg_GLM", "SplineReg_LM", "UnivariateFitter", "visualize_boosting" ], "_datasets": [ { "name": "BaFe2As2", "title": "Barium-Ferrum-Arsenide Powder Diffraction Data", "object": "BaFe2As2", "class": [ "data.frame" ], "fields": [ "angle", "intensity" ], "rows": 1151, "table": true, "tojson": true }, { "name": "coalMining", "title": "Coal Mining Disasters Data", "object": "coalMining", "class": [ "data.frame" ], "fields": [ "years", "accidents" ], "rows": 112, "table": true, "tojson": true }, { "name": "CrystalData10k", "title": "Crystallographic Scattering Data", "object": "CrystalData10k", "class": [ "data.frame" ], "fields": [ "Q", "F_Q" ], "rows": 1721, "table": true, "tojson": true }, { "name": "CrystalData300k", "title": "Crystallographic Scattering Data", "object": "CrystalData300k", "class": [ "data.frame" ], "fields": [ "Q", "F_Q" ], "rows": 1721, "table": true, "tojson": true }, { "name": "EWmortality", "title": "Death Counts in England and Wales", "object": "EWmortality", "class": [ "data.frame" ], "fields": [ "Age", "Deaths", "Exposure" ], "rows": 109, "table": true, "tojson": true } ], "_help": [ { "page": "GeDS-package", "title": "Geometrically Designed Spline Regression", "topics": [ "GeDS-package", "GeDS" ] }, { "page": "BaFe2As2", "title": "Barium-Ferrum-Arsenide Powder Diffraction Data", "topics": [ "BaFe2As2" ] }, { "page": "BivariateFitters", "title": "Fitter Function for GeD Spline Regression for Bivariate Data", "topics": [ "BivariateFitter", "BivariateFitters", "GenBivariateFitter" ] }, { "page": "bl_imp", "title": "Base Learner Importance for GeDSboost Objects", "topics": [ "bl_imp", "bl_imp.GeDSboost" ] }, { "page": "coalMining", "title": "Coal Mining Disasters Data", "topics": [ "coalMining" ] }, { "page": "coef", "title": "Coef Method for GeDS Objects", "topics": [ "coef.GeDS" ] }, { "page": "coef.GeDSgam_GeDSboost", "title": "Coef Method for GeDSgam, GeDSboost", "topics": [ "coef.GeDSboost", "coef.GeDSgam", "coef.GeDSgam,", "coef.GeDSgam,boost" ] }, { "page": "confint.GeDS", "title": "Confidence Intervals for GeDS Models Coefficients", "topics": [ "confint.GeDS", "confint.GeDSboost", "confint.GeDSgam" ] }, { "page": "crossv_GeDS", "title": "K-Fold Cross-Validation", "topics": [ "crossv_GeDS" ] }, { "page": "CrystalData", "title": "Crystallographic Scattering Data", "topics": [ "CrystalData", "CrystalData10k", "CrystalData300k" ] }, { "page": "Derive", "title": "Derivative of GeDS Objects", "topics": [ "Derive" ] }, { "page": "deviance.GeDS", "title": "Deviance Method for GeDS, GeDSgam, GeDSboost", "topics": [ "deviance.GeDS", "deviance.GeDSboost", "deviance.GeDSgam" ] }, { "page": "EWmortality", "title": "Death Counts in England and Wales", "topics": [ "EWmortality" ] }, { "page": "f", "title": "Defining the Covariates for the Spline Component in a GeDS Formula", "topics": [ "f" ] }, { "page": "family.GeDS", "title": "Extract Family from a GeDS, GeDSgam, GeDSboost Object", "topics": [ "family.GeDS", "family.GeDSboost", "family.GeDSgam" ] }, { "page": "formula.GeDS", "title": "Formula for the Predictor Model", "topics": [ "formula.GeDS", "formula.GeDSboost", "formula.GeDSgam" ] }, { "page": "GGeDS", "title": "Generalized Geometrically Designed Spline Regression Estimation", "topics": [ "GGeDS" ] }, { "page": "Integrate", "title": "Defined Integral of GeDS Objects", "topics": [ "Integrate" ] }, { "page": "IRLSfit", "title": "IRLS Estimation", "topics": [ "IRLSfit" ] }, { "page": "knots", "title": "Knots Method for GeDS, GeDSgam, GeDSboost", "topics": [ "knots.GeDS", "knots.GeDSboost", "knots.GeDSgam" ] }, { "page": "lines.GeDS", "title": "Lines Method for GeDS Objects", "topics": [ "lines.GeDS" ] }, { "page": "logLik.GeDS", "title": "Extract Log-Likelihood from a GeDS Object", "topics": [ "logLik.GeDS", "logLik.GeDSboost", "logLik.GeDSgam" ] }, { "page": "n.boost.iter", "title": "Extract Number of Boosting Iterations from a GeDSboost Object", "topics": [ "N.boost.iter", "N.boost.iter.GeDSboost" ] }, { "page": "NGeDS", "title": "Geometrically Designed Spline Regression Estimation", "topics": [ "NGeDS" ] }, { "page": "NGeDSboost", "title": "Component-Wise Gradient Boosting with NGeDS Base-Learners", "topics": [ "NGeDSboost" ] }, { "page": "NGeDSgam", "title": "NGeDSgam: Local Scoring Algorithm with GeD Splines in Backfitting", "topics": [ "NGeDSgam" ] }, { "page": "plot.GeDS", "title": "Plot Method for GeDS Objects", "topics": [ "plot.GeDS" ] }, { "page": "plot.GeDSboost", "title": "Plot Method for GeDSboost Objects", "topics": [ "plot.GeDSboost" ] }, { "page": "plot.GeDSgam", "title": "Plot Method for GeDSgam Objects", "topics": [ "plot.GeDSgam" ] }, { "page": "PPolyRep", "title": "Piecewise Polynomial Spline Representation", "topics": [ "PPolyRep" ] }, { "page": "predict.GeDS", "title": "Predict Method for GeDS Objects", "topics": [ "predict.GeDS" ] }, { "page": "predict.GeDSgam_GeDSboost", "title": "Predict Method for GeDSgam, GeDSboost", "topics": [ "predict.GeDSboost", "predict.GeDSboost,", "predict.GeDSgam", "predict.GeDSgam,boost" ] }, { "page": "print.GeDS", "title": "Print Method for GeDS, GeDSgam, GeDSboost", "topics": [ "print.GeDS", "print.GeDSboost", "print.GeDSgam" ] }, { "page": "SplineReg", "title": "Estimation for Models with Spline and Parametric Components", "topics": [ "SplineReg", "SplineReg_GLM", "SplineReg_LM" ] }, { "page": "summary.GeDS", "title": "Summary Method for GeDS, GeDSgam, GeDSboost", "topics": [ "summary.GeDS", "summary.GeDSboost", "summary.GeDSgam" ] }, { "page": "UnivariateFitters", "title": "Functions Used to Fit GeDS Objects with a Univariate Spline Regression", "topics": [ "Fitters", "GenUnivariateFitter", "UnivariateFitter", "UnivariateFitters" ] }, { "page": "visualize_boosting", "title": "Visualize Boosting Iterations", "topics": [ "visualize_boosting", "visualize_boosting.GeDSboost" ] } ], "_readme": "https://github.com/emilioluissaenzguillen/geds/raw/HEAD/README.md", "_rundeps": [ "codetools", "digest", "doFuture", "doParallel", "doRNG", "foreach", "Formula", "future", "future.apply", "globals", "inum", "iterators", "lattice", "libcoin", "listenv", "MASS", "Matrix", "mboost", "misc3d", "mvtnorm", "nnls", "parallelly", "partykit", "plot3D", "quadprog", "Rcpp", "rngtools", "rpart", "stabs", "survival" ], "_sysdeps": [ { "shlib": "libstdc++", "package": "libstdc++6", "source": "gcc", "version": "14.2.0-4ubuntu2~24.04.1", "name": "c++", "homepage": "http://gcc.gnu.org/", "description": "GNU Standard C++ Library v3" } ], "_vignettes": [ { "source": "rpubs_GeDSboost.Rmd", "filename": "rpubs_GeDSboost.html", "title": "Functional Gradient Boosting (FGB) with GeD Splines", "author": "Emilio Luis Sáenz Guillén", "engine": "knitr::rmarkdown", "headings": [ "1. Introduction", "2. Functional Gradient Boosting", "2.1. Boosting with Splines", "2.2. Componentwise Functional Gradient Boosting", "3. Overcoming key limitations of Gradient Boosting with FGB-GeDS", "4. Functional Gradient Boosting with GeDS base-learners", "4.1. FGB-GeDS for regression", "4.2. FGB-GeDS for classification", "4.2.1. Two classes", "4.2.2. Multinomial classification" ], "created": "2025-09-08 15:03:05", "modified": "2025-09-08 15:03:05", "commits": 1 }, { "source": "jss_article.pdf.asis", "filename": "jss_article.pdf", "title": "GeDS: Regression, GAMs and Boosting using Geometrically Designed Splines", "engine": "R.rsp::asis", "headings": [], "created": "2025-04-16 09:03:06", "modified": "2025-06-17 13:41:10", "commits": 2 }, { "source": "rpubs_GeDSgam.Rmd", "filename": "rpubs_GeDSgam.html", "title": "Generalized Additive Models (GAM) with GeD Splines", "author": "Emilio Luis Sáenz Guillén", "engine": "knitr::rmarkdown", "headings": [ "Introduction", "What are Generalized Additive Models?", "Linear Regression and GLM", "Additive models and GAM", "Generalized Additive Models fitting: Local-Scoring and Backfitting", "1. Backfitting", "2. Local Scoring", "Generalized Additive Models with GeD Splines" ], "created": "2025-09-08 15:03:05", "modified": "2025-09-17 06:33:10", "commits": 2 }, { "source": "rpubs_GeDS.Rmd", "filename": "rpubs_GeDS.html", "title": "Geometrically Designed Splines: GeDS", "author": "Emilio Luis Sáenz Guillén", "engine": "knitr::rmarkdown", "headings": [ "Introduction", "What are (polynomial) splines?", "Normal GeD Spline Regression", "Stage A", "Stage B", "Generalized GeD Spline Regression", "The GNM (GLM) fitting problem", "Generalized GeDS estimation", "Poisson:", "Gamma:", "(Quasi)Binomial:", "Bivariate GeD Spline Regression", "Normal Bivariate GeDS:", "Poisson Bivariate GeDS:" ], "created": "2025-09-08 15:03:05", "modified": "2025-09-08 15:03:05", "commits": 1 } ], "_score": 6.28330122870355, "_indexed": true, "_nocasepkg": "geds", "_universes": [ "emilioluissaenzguillen" ], "_binaries": [ { "r": "4.7.0", "os": "linux", "version": "0.3.4", "date": "2026-06-06T06:10:14.000Z", "distro": "noble", "arch": "aarch64", "commit": "dd12c06c044d53aba45afe6c7a4ae2f29e92205b", "fileid": "21ca7cde4340532bb888dc03364687bd6e4be9a432f0fdd0b94594ebdc4a4a7f", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/emilioluissaenzguillen/actions/runs/27054217010" }, { "r": "4.7.0", "os": "linux", "version": "0.3.4", "date": "2026-06-06T05:57:40.000Z", "distro": "noble", "arch": "x86_64", "commit": "dd12c06c044d53aba45afe6c7a4ae2f29e92205b", "fileid": "f44487a971d8c800764da7b50dbbd1b8acbbf6117b7ceb8acff7ebf6afbf986d", "status": "failure", "check": "ERROR", "buildurl": "https://github.com/r-universe/emilioluissaenzguillen/actions/runs/27054217010" }, { "r": "4.6.0", "os": "linux", "version": "0.3.4", "date": "2026-06-06T06:10:14.000Z", "distro": "noble", "arch": "aarch64", "commit": "dd12c06c044d53aba45afe6c7a4ae2f29e92205b", "fileid": "a03a98edc56bda6f320840323db80284239fb369a2dd659173849fcba90da6c8", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/emilioluissaenzguillen/actions/runs/27054217010" }, { "r": "4.6.0", "os": "linux", "version": "0.3.4", "date": "2026-06-06T05:57:36.000Z", "distro": "noble", "arch": "x86_64", "commit": "dd12c06c044d53aba45afe6c7a4ae2f29e92205b", "fileid": "cabd33b47f15ddfa9aed5d7308a027b114cbfa14c8969d96f8f2ba38199a4949", "status": "failure", "check": "ERROR", "buildurl": "https://github.com/r-universe/emilioluissaenzguillen/actions/runs/27054217010" }, { "r": "4.5.3", "os": "mac", "version": "0.3.4", "date": "2026-06-06T05:57:46.000Z", "arch": "aarch64", "commit": "dd12c06c044d53aba45afe6c7a4ae2f29e92205b", "fileid": "5d393a4f674197f7151414c7686a8137f3c3835dadf52b8121892f017d1fa59d", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/emilioluissaenzguillen/actions/runs/27054217010" }, { "r": "4.5.3", "os": "mac", "version": "0.3.4", "date": "2026-06-06T05:59:13.000Z", "arch": "x86_64", "commit": "dd12c06c044d53aba45afe6c7a4ae2f29e92205b", "fileid": "97e0ecb563a25350c8e19102e2bcd8c4322edbe50e8a02319ad5569361aabc4b", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/emilioluissaenzguillen/actions/runs/27054217010" }, { "r": "4.6.0", "os": "mac", "version": "0.3.4", "date": "2026-06-06T05:58:23.000Z", "arch": "aarch64", "commit": "dd12c06c044d53aba45afe6c7a4ae2f29e92205b", "fileid": "9177af3cdebfbd8bf830f2086abe509ecacf198d7699cb30940eba50f9fcb1d7", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/emilioluissaenzguillen/actions/runs/27054217010" }, { "r": "4.6.0", "os": "mac", "version": "0.3.4", "date": "2026-06-06T05:59:31.000Z", "arch": "x86_64", "commit": "dd12c06c044d53aba45afe6c7a4ae2f29e92205b", "fileid": "524369670c5d6c647f70bd657c123f0393f10c801c6e6960558280cf06c3da99", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/emilioluissaenzguillen/actions/runs/27054217010" }, { "r": "4.6.0", "os": "wasm", "version": "0.3.4", "date": "2026-06-06T05:57:38.000Z", "arch": "emscripten", "commit": "dd12c06c044d53aba45afe6c7a4ae2f29e92205b", "fileid": "226c4496977313af52de5d7c737c7e96c895a5a66a7c2d45d0a0e1a3d5b01a71", "status": "success", "buildurl": "https://github.com/r-universe/emilioluissaenzguillen/actions/runs/27054217010" }, { "r": "4.7.0", "os": "win", "version": "0.3.4", "date": "2026-06-06T05:56:47.000Z", "arch": "x86_64", "commit": "dd12c06c044d53aba45afe6c7a4ae2f29e92205b", "fileid": "dcd9ba81eed2fd04909681cdc121c86a9c1690351b638da45aaf060da850fc35", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/emilioluissaenzguillen/actions/runs/27054217010" }, { "r": "4.5.3", "os": "win", "version": "0.3.4", "date": "2026-06-06T05:56:44.000Z", "arch": "x86_64", "commit": "dd12c06c044d53aba45afe6c7a4ae2f29e92205b", "fileid": "22424a803887dddd299470fc23083d95093f567f576fe56972c5dc086dc2c093", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/emilioluissaenzguillen/actions/runs/27054217010" }, { "r": "4.6.0", "os": "win", "version": "0.3.4", "date": "2026-06-06T05:56:55.000Z", "arch": "x86_64", "commit": "dd12c06c044d53aba45afe6c7a4ae2f29e92205b", "fileid": "9056e5bda63fba84ff1c9c4b8c7e0ec56621668441c2e529a9b1d6d3f58820d3", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/emilioluissaenzguillen/actions/runs/27054217010" } ] }