{ "_id": "6ad181fba6a61792ed76b4c6", "Package": "timesift", "Title": "Learn Predictive Representations of Time-Varying Data", "Version": "0.6.0", "Authors@R": "person(\"Gilles\", \"Colling\", email = \"gilles.colling051@gmail.com\",\nrole = c(\"aut\", \"cre\", \"cph\"),\ncomment = c(ORCID = \"0000-0003-3070-6066\"))", "Description": "Fits and compares representations of time-varying data\nagainst a prediction target. Given a table of targets and a\ntable of time-stamped series belonging to them, it builds each\ncandidate representation, from the record unreduced through a\ncalendar grain such as a week or a month to a lookback anchored\non each target, fits the requested learners on each, scores\nevery candidate on one set of held-out folds, and stacks the\nout-of-fold predictions into an ensemble. Calendar-aware\nbinning keeps a bin a real week or month rather than a fixed\nblock of hours. Learners, response heads and metrics are\nregistered rather than hard-coded, so adding one is a\nregistration and not a fork of the fitting code. The penalised\nbaseline is an elastic net fitted by cyclic coordinate descent\nalong a warm-started path, following Friedman, Hastie and\nTibshirani (2010) . The shipped\ndefault is presence-absence with a joint multi-label head, the\nsetting used for species distribution modelling from\nmicroclimate loggers, scored by the area under the ROC curve\nwith the true skill statistic of Allouche, Tsoar and Kadmon\n(2006) beside it.", "License": "MIT + file LICENSE", "Encoding": "UTF-8", "Language": "en-GB", "Roxygen": "list(markdown = TRUE)", "URL": "https://gillescolling.com/timesift/,\nhttps://github.com/gcol33/timesift", "BugReports": "https://github.com/gcol33/timesift/issues", "VignetteBuilder": "knitr", "Config/testthat/edition": "3", "Config/roxygen2/version": "8.1.0", "Repository": "https://gcol33.r-universe.dev", "Date/Publication": "2026-10-16 01:19:01 UTC", "RemoteUrl": "https://github.com/gcol33/timesift", "RemoteRef": "HEAD", "RemoteSha": "88f0d1d029c9d4640bb47ad7fdc3a69ae043a5fa", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-10-16 01:36:58 UTC", "User": "root" }, "Author": "Gilles Colling [aut, cre, cph] (ORCID:\n)", "Maintainer": "Gilles Colling ", "_user": "gcol33", "_type": "src", "_file": "timesift_0.6.0.tar.gz", "_fileid": "https://r2.ropensci.org/78529c6dbc66c3c606f8cc43552bc15e2fdac28f463ff840bfd08b846783e528", "_filesize": 2214277, "_sha256": "78529c6dbc66c3c606f8cc43552bc15e2fdac28f463ff840bfd08b846783e528", "_expires": "2027-01-24T01:46:32.000Z", "_created": "2026-10-16T01:36:58.000Z", "_published": "2026-10-16T01:46:34.947Z", "_buildurl": "https://github.com/r-universe/gcol33/actions/runs/38835669380", "_status": "success", "_host": "GitHub-Actions", "_jobs": [ { "job": 116765718720, "time": 196, "config": "linux-devel-arm64", "r": "4.7.0", "check": "OK", "artifact": "12018873948" }, { "job": 116765718781, "time": 222, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "12018829142" }, { "job": 116765718754, "time": 176, "config": "linux-release-arm64", "r": "4.6.1", "check": "OK", "artifact": "12019431587" }, { "job": 116765718816, "time": 214, "config": "linux-release-x86_64", "r": "4.6.1", "check": "OK", "artifact": "12019397856" }, { "job": 116765719174, "time": 181, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "OK", "artifact": "12019158123" }, { "job": 116765718790, "time": 395, "config": "macos-oldrel-x86_64", "r": "4.5.3", "check": "OK", "artifact": "12019098623" }, { "job": 116765718724, "time": 159, "config": "macos-release-arm64", "r": "4.6.1", "check": "OK", "artifact": "12019656228" }, { "job": 116765718705, "time": 471, "config": "macos-release-x86_64", "r": "4.6.1", "check": "OK", "artifact": "12019756105" }, { "job": 116764202419, "time": 322, "config": "source", "r": "4.6.1", "check": "OK", "artifact": "12019187499" }, { "job": 116765718543, "time": 132, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "12019652102" }, { "job": 116765718690, "time": 352, "config": "windows-devel-arm64", "r": "4.7.0", "check": "OK", "artifact": "12019008771" }, { "job": 116765718674, "time": 277, "config": "windows-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "12019443002" }, { "job": 116765718672, "time": 175, "config": "windows-oldrel-x86_64", "r": "4.5.3", "check": "OK", "artifact": "12019780779" }, { "job": 116765718779, "time": 317, "config": "windows-release-arm64", "r": "4.6.1", "check": "OK", "artifact": "12019212019" }, { "job": 116765718749, "time": 179, "config": "windows-release-x86_64", "r": "4.6.1", "check": "OK", "artifact": "12019361757" } ], "_upstream": "https://github.com/gcol33/timesift", "_commit": { "id": "88f0d1d029c9d4640bb47ad7fdc3a69ae043a5fa", "author": "Gilles Colling ", "committer": "Gilles Colling ", "message": "test: Gouriéroux joins the spelling word list\n\nCo-Authored-By: Claude Sonnet 5.5 \n", "time": 1792113541 }, "_maintainer": { "name": "Gilles Colling", "email": "gilles.colling051@gmail.com", "login": "gcol33", "linkedin": "in/gilles-colling-0b3747306", "orcid": "0000-0003-3070-6066", "twitter": "@Gilles__Colling", "description": "Building tools for ecology in R, C++, and Rust.\nWhy did Microsoft have to buy it. Sadge :(", "uuid": 25009600 }, "_distro": "resolute", "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 4.1", "role": "Depends" }, { "package": "cpp11", "role": "LinkingTo" }, { "package": "grDevices", "role": "Imports" }, { "package": "graphics", "role": "Imports" }, { "package": "rlang", "role": "Imports" }, { "package": "stats", "role": "Imports" }, { "package": "tidyselect", "version": ">= 1.2.0", "role": "Imports" }, { "package": "tools", "role": "Imports" }, { "package": "utils", "role": "Imports" }, { "package": "testthat", "version": ">= 3.0.0", "role": "Suggests" }, { "package": "torch", "role": "Suggests" }, { "package": "glmnet", "role": "Suggests" }, { "package": "lme4", "role": "Suggests" }, { "package": "lmerTest", "role": "Suggests" }, { "package": "emmeans", "role": "Suggests" }, { "package": "knitr", "role": "Suggests" }, { "package": "rmarkdown", "role": "Suggests" }, { "package": "spelling", "role": "Suggests" }, { "package": "terra", "role": "Suggests" }, { "package": "withr", "role": "Suggests" } ], "_owner": "gcol33", "_selfowned": true, "_usedby": 0, "_updates": [ { "week": "2026-36", "n": 22 }, { "week": "2026-37", "n": 52 }, { "week": "2026-38", "n": 23 }, { "week": "2026-39", "n": 27 }, { "week": "2026-40", "n": 63 }, { "week": "2026-41", "n": 15 }, { "week": "2026-42", "n": 28 } ], "_tags": [ { "name": "v0.3.0", "date": "2026-09-21" }, { "name": "v0.3.1", "date": "2026-09-22" }, { "name": "v0.3.2", "date": "2026-09-25" }, { "name": "v0.4.0", "date": "2026-09-28" }, { "name": "v0.5.0", "date": "2026-10-03" }, { "name": "v0.6.0", "date": "2026-10-06" } ], "_topics": [ "microclimate", "python", "species-distribution-modelling", "time-series", "cpp" ], "_stars": 0, "_contributors": [ { "user": "gcol33", "count": 228, "uuid": 25009600 }, { "user": "claude", "count": 2, "uuid": 81847 } ], "_userbio": { "uuid": 25009600, "type": "user", "name": "Gilles Colling", "followers": 10, "description": "Building tools for ecology in R, C++, and Rust.\nWhy did Microsoft have to buy it. Sadge :(" }, "_downloads": { "count": 236, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/timesift" }, "_devurl": "https://github.com/gcol33/timesift", "_pkgdown": "https://gillescolling.com/timesift/", "_searchresults": 18, "_rbuild": "4.6.1", "_assets": [ "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "extra/manual.pdf", "extra/NEWS.html", "extra/NEWS.txt", "extra/readme.html", "extra/readme.md", "extra/timesift.html", "LICENSE" ], "_homeurl": "https://github.com/gcol33/timesift", "_realowner": "gcol33", "_cranurl": true, "_releases": [ { "version": "0.3.1", "date": "2026-10-02" } ], "_exports": [ "additive", "all_of", "any_of", "as_sift", "average_precision", "bind_channels", "block_cv", "boosting", "boyce_index", "build_representation", "calendar_channels", "cnn", "contains", "coverage", "cv", "decision_threshold", "digest_array", "discriminant", "elasticnet", "ends_with", "ensemble", "ensemble_combine", "ensemble_fit", "ensemble_spread", "ensemble_weights", "env_cv", "envelope", "everything", "feature_matrix", "fit_learner", "fold_map", "forest", "grain", "grain_contrasts", "grain_ladder", "grain_matrix", "grains", "grouped_cv", "hierarchical", "implied_skill", "kappa_score", "learner", "learners", "linear", "lookback", "lookback_matrix", "lookbacks", "mars", "matches", "maxent", "metrics", "mlp", "model_agreement", "multigrain", "native", "occlusion", "ordinal_metric", "paired_contrast", "perceptron", "positive_weights", "project", "pseudo_absences", "range_change", "read_cells", "read_folds", "read_response", "register_learner", "register_metric", "register_response", "register_tuning", "regression_metric", "rescnn", "response_curve", "responses", "roc_auc", "scorable_cells", "score_predictions", "select_grain", "simulate_records", "starts_with", "table_metric", "timesift", "timesift_set", "train_control", "tree", "tss", "tss_inflation", "tune", "tunings", "where", "write_cells", "write_folds", "write_response" ], "_help": [ { "page": "additive", "title": "Generalised additive model on the flattened representation", "topics": [ "additive" ] }, { "page": "artifacts", "title": "Read and write the artifacts that cross the language boundary", "topics": [ "artifacts", "read_cells", "read_folds", "read_response", "write_cells", "write_folds", "write_response" ] }, { "page": "as.matrix.timesift_matrix", "title": "A representation as a block of predictors", "topics": [ "as.matrix.timesift_matrix" ] }, { "page": "average_precision", "title": "Average precision", "topics": [ "average_precision" ] }, { "page": "bind_channels", "title": "Put channels side by side", "topics": [ "bind_channels" ] }, { "page": "boosting", "title": "Gradient boosted trees on the flattened representation", "topics": [ "boosting" ] }, { "page": "boyce_index", "title": "The continuous Boyce index", "topics": [ "boyce_index" ] }, { "page": "build_representation", "title": "Build one representation for a set of targets", "topics": [ "build_representation" ] }, { "page": "calendar_channels", "title": "Where in the year, or the day, each bin sits", "topics": [ "calendar_channels" ] }, { "page": "combine", "title": "Combine learners, or representations, into a set", "topics": [ "c.timesift_learner", "c.timesift_models", "c.timesift_representation", "c.timesift_sift", "combine" ] }, { "page": "coverage", "title": "Which units reach which bins", "topics": [ "coverage" ] }, { "page": "cv", "title": "How the folds are drawn", "topics": [ "block_cv", "cv", "env_cv", "grouped_cv" ] }, { "page": "digest_array", "title": "The cross-language digest of a representation", "topics": [ "digest_array" ] }, { "page": "discriminant", "title": "Flexible discriminant analysis on the flattened representation", "topics": [ "discriminant" ] }, { "page": "elasticnet", "title": "Penalised regression on the flattened representation", "topics": [ "elasticnet" ] }, { "page": "ensemble", "title": "How the candidates are combined", "topics": [ "ensemble" ] }, { "page": "ensemble_combine", "title": "Combine one prediction per member into one prediction", "topics": [ "ensemble_combine" ] }, { "page": "ensemble_fit", "title": "Fit the combiner on the out-of-fold predictions", "topics": [ "ensemble_fit" ] }, { "page": "ensemble_spread", "title": "How far the members of an ensemble disagree", "topics": [ "ensemble_spread" ] }, { "page": "ensemble_weights", "title": "The weights the combiner fitted", "topics": [ "ensemble_weights" ] }, { "page": "envelope", "title": "biomod2's surface range envelope on the flattened representation", "topics": [ "envelope" ] }, { "page": "feature_matrix", "title": "Bring an already-reduced feature table into a ladder", "topics": [ "feature_matrix" ] }, { "page": "fit_learner", "title": "Fit one learner at one grain", "topics": [ "fit_learner", "predict.timesift_fit" ] }, { "page": "fold_map", "title": "Assign units to cross-validation folds", "topics": [ "fold_map" ] }, { "page": "forest", "title": "Random forest on the flattened representation", "topics": [ "forest" ] }, { "page": "grain_contrasts", "title": "Compare every grain against a learner's best one", "topics": [ "grain_contrasts" ] }, { "page": "grain_ladder", "title": "Fit at every grain and see where skill saturates", "topics": [ "grain_ladder", "summary.timesift_ladder" ] }, { "page": "grain_matrix", "title": "Reduce sensor series to a temporal grain", "topics": [ "grain_matrix" ] }, { "page": "grains", "title": "Several representations to run the same learners across", "topics": [ "as_sift", "grains", "lookbacks" ] }, { "page": "hierarchical", "title": "Bayesian logistic model with a spatial field", "topics": [ "hierarchical" ] }, { "page": "implied_skill", "title": "What population skill a reported level is consistent with", "topics": [ "implied_skill" ] }, { "page": "kappa_score", "title": "Cohen's kappa, and where two models disagree", "topics": [ "decision_threshold", "decision_threshold.default", "decision_threshold.timesift", "kappa_score", "model_agreement" ] }, { "page": "learner", "title": "Define a learner", "topics": [ "learner" ] }, { "page": "linear", "title": "A generalised linear model on the flattened representation", "topics": [ "linear" ] }, { "page": "lookback_matrix", "title": "Reduce sensor series to a lookback anchored on each target", "topics": [ "lookback_matrix" ] }, { "page": "mars", "title": "Multivariate adaptive regression splines on the flattened representation", "topics": [ "mars" ] }, { "page": "maxent", "title": "Maxent on the flattened representation", "topics": [ "maxent" ] }, { "page": "native", "title": "How a series becomes an array a learner reads", "topics": [ "grain", "lookback", "multigrain", "native" ] }, { "page": "occlusion", "title": "What part of the record a fitted model reads", "topics": [ "occlusion", "occlusion.default", "occlusion.timesift", "occlusion.timesift_ladder" ] }, { "page": "ordinal_metric", "title": "A metric of ordinal classes", "topics": [ "ordinal_metric" ] }, { "page": "paired_contrast", "title": "Compare two arms cell by cell", "topics": [ "paired_contrast" ] }, { "page": "perceptron", "title": "Single-hidden-layer network on the flattened representation", "topics": [ "perceptron" ] }, { "page": "plot.timesift", "title": "Draw a run", "topics": [ "plot.timesift" ] }, { "page": "plot.timesift_ladder", "title": "Draw a ladder", "topics": [ "plot.timesift_ladder" ] }, { "page": "plot.timesift_range_change", "title": "Draw the change in range", "topics": [ "plot.timesift_range_change" ] }, { "page": "plot.timesift_response_curve", "title": "Draw a response curve", "topics": [ "plot.timesift_response_curve" ] }, { "page": "plot.timesift_selection", "title": "Draw how stable the choice of grain was", "topics": [ "plot.timesift_selection" ] }, { "page": "positive_weights", "title": "Case weights that balance a rare response", "topics": [ "positive_weights" ] }, { "page": "predict.timesift", "title": "Predict from a fitted timesift", "topics": [ "predict.timesift" ] }, { "page": "project", "title": "Project a fit onto rasters", "topics": [ "project" ] }, { "page": "pseudo_absences", "title": "Draw pseudo-absences from a pool of background units", "topics": [ "pseudo_absences" ] }, { "page": "range_change", "title": "How the range of each response changes between two projections", "topics": [ "range_change" ] }, { "page": "register_learner", "title": "Register a learner", "topics": [ "learners", "register_learner" ] }, { "page": "register_metric", "title": "Register a metric", "topics": [ "metrics", "register_metric" ] }, { "page": "register_response", "title": "Register a response head", "topics": [ "register_response", "responses" ] }, { "page": "register_tuning", "title": "Register the grid a learner is tuned over", "topics": [ "register_tuning", "tunings" ] }, { "page": "regression_metric", "title": "A metric of a numeric response and its predictions", "topics": [ "regression_metric" ] }, { "page": "response_curve", "title": "How a prediction responds to one predictor", "topics": [ "response_curve", "response_curve.default", "response_curve.timesift" ] }, { "page": "roc_auc", "title": "The area under the ROC curve", "topics": [ "roc_auc" ] }, { "page": "scorable_cells", "title": "Which cells a score is defined on", "topics": [ "scorable_cells" ] }, { "page": "score_predictions", "title": "Score held-out predictions on the cells the mask allows", "topics": [ "score_predictions" ] }, { "page": "select_grain", "title": "Choose the grain inside the training data, and score the whole procedure", "topics": [ "select_grain", "summary.timesift_selection" ] }, { "page": "simulate_records", "title": "Simulate sensor records whose response acts at a known temporal grain", "topics": [ "simulate_records" ] }, { "page": "table_metric", "title": "A metric of the two-by-two table of decisions", "topics": [ "table_metric" ] }, { "page": "timesift", "title": "Fit and compare representations of time-varying data", "topics": [ "timesift" ] }, { "page": "timesift_report", "title": "What a run found", "topics": [ "print.timesift", "print.timesift_summary", "summary.timesift", "timesift_report" ] }, { "page": "timesift_set", "title": "Several built representations of the same targets", "topics": [ "timesift_set" ] }, { "page": "torch_learners", "title": "Sequence encoders with a joint multi-label head", "topics": [ "cnn", "mlp", "rescnn", "torch_learners" ] }, { "page": "train_control", "title": "Training settings every neural learner reads", "topics": [ "train_control" ] }, { "page": "tree", "title": "Classification and regression tree on the flattened representation", "topics": [ "tree" ] }, { "page": "tss", "title": "The true skill statistic", "topics": [ "tss" ] }, { "page": "tss_inflation", "title": "How much a self-selected threshold inflates the reported level", "topics": [ "tss_inflation" ] }, { "page": "tune", "title": "Tune a learner's settings on the training units", "topics": [ "tune" ] } ], "_pkglogo": "https://github.com/gcol33/timesift/raw/HEAD/man/figures/logo.svg", "_readme": "https://github.com/gcol33/timesift/raw/HEAD/README.md", "_rundeps": [ "cli", "cpp11", "glue", "lifecycle", "rlang", "tidyselect", "vctrs", "withr" ], "_sysdeps": [ { "shlib": "libstdc++", "package": "libstdc++6", "source": "gcc", "version": "16-20260322-1ubuntu1", "name": "c++", "homepage": "http://gcc.gnu.org/", "description": "GNU Standard C++ Library v3" } ], "_vignettes": [ { "source": "timesift.Rmd", "filename": "timesift.html", "title": "Choosing how a record is read", "engine": "knitr::rmarkdown", "headings": [ "Installation", "Two tables", "One call", "Representations", "Compatible model inputs", "The split, and the cells a score is defined on", "Learners, and how they are trained", "The combination", "TSS and threshold selection", "The arrays on their own", "Daily and individual extremes", "One grain at a time", "Identifying influential bins", "Where next" ], "created": "2026-09-04 01:15:26", "modified": "2026-10-15 11:48:23", "commits": 17 } ], "_score": 4.799340549453582, "_indexed": true, "_nocasepkg": "timesift", "_universes": [ "gcol33" ], "_binaries": [ { "r": "4.7.0", "os": "linux", "version": "0.6.0", "date": "2026-10-16T01:39:49.000Z", "distro": "resolute", "arch": "aarch64", "commit": "88f0d1d029c9d4640bb47ad7fdc3a69ae043a5fa", "fileid": "https://r2.ropensci.org/eba455012196460707672b312c98d8ecdcf4e73ef65818378360aaaa1f5be270", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/gcol33/actions/runs/38835669380" }, { "r": "4.7.0", "os": "linux", "version": "0.6.0", "date": "2026-10-16T01:39:56.000Z", "distro": "resolute", "arch": "x86_64", "commit": "88f0d1d029c9d4640bb47ad7fdc3a69ae043a5fa", "fileid": "https://r2.ropensci.org/36e7cf4d7cb9d9f0ef5f73fb39840831a316fdb12501e3e2a8234d469b7c3ecf", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/gcol33/actions/runs/38835669380" }, { "r": "4.6.1", "os": "linux", "version": "0.6.0", "date": "2026-10-16T01:39:34.000Z", "distro": "resolute", "arch": "aarch64", "commit": "88f0d1d029c9d4640bb47ad7fdc3a69ae043a5fa", "fileid": "https://r2.ropensci.org/8e7bce7ecbe04fbecf165a7087b11135243cdbaed9a85c02bb1a33bb2e617ce9", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/gcol33/actions/runs/38835669380" }, { "r": "4.6.1", "os": "linux", "version": "0.6.0", "date": "2026-10-16T01:39:51.000Z", "distro": "resolute", "arch": "x86_64", "commit": "88f0d1d029c9d4640bb47ad7fdc3a69ae043a5fa", "fileid": "https://r2.ropensci.org/b8c06c5616eab78521d158422e67db3d6462cb5a1e2f274b1c3e83e4ed3029ce", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/gcol33/actions/runs/38835669380" }, { "r": "4.5.3", "os": "mac", "version": "0.6.0", "date": "2026-10-16T01:39:47.000Z", "arch": "aarch64", "commit": "88f0d1d029c9d4640bb47ad7fdc3a69ae043a5fa", "fileid": "https://r2.ropensci.org/9a8ffc21045f997f601fb58171f946dc854732f2f2377083bcc7b7bf03bfea6b", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/gcol33/actions/runs/38835669380" }, { "r": "4.5.3", "os": "mac", "version": "0.6.0", "date": "2026-10-16T01:41:27.000Z", "arch": "x86_64", "commit": "88f0d1d029c9d4640bb47ad7fdc3a69ae043a5fa", "fileid": "https://r2.ropensci.org/aebbb66ddec634084656984e3a5301cc06e8b2351a6541be0325106c0818a18a", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/gcol33/actions/runs/38835669380" }, { "r": "4.6.1", "os": "mac", "version": "0.6.0", "date": "2026-10-16T01:39:27.000Z", "arch": "aarch64", "commit": "88f0d1d029c9d4640bb47ad7fdc3a69ae043a5fa", "fileid": "https://r2.ropensci.org/1ce65d0c5b06ac3f74619cb1134b69d3ad6d306eb8dedc565d09ccfe7fc23c2f", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/gcol33/actions/runs/38835669380" }, { "r": "4.6.1", "os": "mac", "version": "0.6.0", "date": "2026-10-16T01:41:55.000Z", "arch": "x86_64", "commit": "88f0d1d029c9d4640bb47ad7fdc3a69ae043a5fa", "fileid": "https://r2.ropensci.org/26e68ef19779cc00902b9d2efae3ba1e807a056a0b5c5288fc0e21242e331fe7", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/gcol33/actions/runs/38835669380" }, { "r": "4.6.0", "os": "wasm", "version": "0.6.0", "date": "2026-10-16T01:39:45.000Z", "arch": "emscripten", "commit": "88f0d1d029c9d4640bb47ad7fdc3a69ae043a5fa", "fileid": "https://r2.ropensci.org/ff23708371c1b3b76ff8d360a81d7fdc4f5bd999f4b5054043653573a5cf8e33", "status": "success", "buildurl": "https://github.com/r-universe/gcol33/actions/runs/38835669380" }, { "r": "4.7.0", "os": "win", "version": "0.6.0", "date": "2026-10-16T01:41:23.000Z", "arch": "aarch64", "commit": "88f0d1d029c9d4640bb47ad7fdc3a69ae043a5fa", "fileid": "https://r2.ropensci.org/eff362d3878b7dc5117581a260c68e7ac76d27d53ffc3d7a232b7fbded1184e4", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/gcol33/actions/runs/38835669380" }, { "r": "4.7.0", "os": "win", "version": "0.6.0", "date": "2026-10-16T01:39:02.000Z", "arch": "x86_64", "commit": "88f0d1d029c9d4640bb47ad7fdc3a69ae043a5fa", "fileid": "https://r2.ropensci.org/b8c9f0fdc0a9c9f2cc1c5f29f2d851544a6e110d4db0df945fad040e60acb474", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/gcol33/actions/runs/38835669380" }, { "r": "4.5.3", "os": "win", "version": "0.6.0", "date": "2026-10-16T01:38:54.000Z", "arch": "x86_64", "commit": "88f0d1d029c9d4640bb47ad7fdc3a69ae043a5fa", "fileid": "https://r2.ropensci.org/3597017fc56e34fac9fcb872e7cebbe463d1a125d698518b3a0befc7d1d082d3", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/gcol33/actions/runs/38835669380" }, { "r": "4.6.1", "os": "win", "version": "0.6.0", "date": "2026-10-16T01:41:02.000Z", "arch": "aarch64", "commit": "88f0d1d029c9d4640bb47ad7fdc3a69ae043a5fa", "fileid": "https://r2.ropensci.org/a37c8b6eeb3be52349ee91d7f7b429867386d61572869d761839b66a798869b1", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/gcol33/actions/runs/38835669380" }, { "r": "4.6.1", "os": "win", "version": "0.6.0", "date": "2026-10-16T01:39:20.000Z", "arch": "x86_64", "commit": "88f0d1d029c9d4640bb47ad7fdc3a69ae043a5fa", "fileid": "https://r2.ropensci.org/1ed21ab4a66458a3aeb07073b92be938785a5439c3b58d69e0805be8a0c9bf57", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/gcol33/actions/runs/38835669380" } ] }