{
"_id": "6ad789cad556c3a3418a781d",
"Package": "nadir",
"Type": "Package",
"Title": "Super Learning with Flexible Formulas",
"Version": "1.0.0",
"Authors@R": "c(\nperson(\"Christian\", \"Testa\", , \"ctesta@hsph.harvard.edu\", role = c(\"aut\", \"cre\"),\ncomment = c(ORCID = '0000-0001-9103-5839')\n),\nperson(\"Nima\", \"Hejazi\", , \"nhejazi@hsph.harvard.edu\", role = c('ths', 'aut'),\ncomment = c(ORCID = '0000-0002-7127-2789')\n))",
"Description": "A functional programming based implementation of the super\nlearner algorithm with an emphasis on supporting the use of\nformulas to specify learners. This approach offers several\nimprovements compared to past implementations including the\nability to easily use random-effects specified in formulas\n(like y ~ (age | strata) + ...) and construction of new\nlearners is as simple as writing and passing a new function.\nThe super learner algorithm was originally described in van der\nLaan et al. (2007)\n.",
"License": "MIT + file LICENSE",
"Encoding": "UTF-8",
"Roxygen": "list (markdown = TRUE, roclets = c (\"namespace\", \"rd\",\n\"srr::srr_stats_roclet\"))",
"VignetteBuilder": "knitr",
"Config/testthat/edition": "3",
"URL": "https://ctesta01.github.io/nadir/,\nhttps://github.com/ctesta01/nadir/",
"BugReports": "https://github.com/ctesta01/nadir/issues",
"Config/roxygen2/version": "8.1.0",
"Config/pak/sysreqs": "libicu-dev",
"Repository": "https://ropensci-staging.r-universe.dev",
"Date/Publication": "2026-10-13 21:24:49 UTC",
"RemoteUrl": "https://github.com/ctesta01/nadir",
"RemoteRef": "HEAD",
"RemoteSha": "d2eef7b6ba6095107ac848842480acbd4ff37b45",
"NeedsCompilation": "no",
"Packaged": {
"Date": "2026-10-20 15:24:31 UTC",
"User": "root"
},
"Author": "Christian Testa [aut, cre] (ORCID:\n),\nNima Hejazi [ths, aut] (ORCID: )",
"Maintainer": "Christian Testa ",
"_user": "ropensci-staging",
"_type": "src",
"_file": "nadir_1.0.0.tar.gz",
"_fileid": "https://r2.ropensci.org/76339e2733065df0fc0ca8ea40dbc3224a690cd32d89f1b7b3e4e2b64957b441",
"_filesize": 2271609,
"_sha256": "76339e2733065df0fc0ca8ea40dbc3224a690cd32d89f1b7b3e4e2b64957b441",
"_expires": "2027-01-28T15:33:28.000Z",
"_created": "2026-10-20T15:24:31.000Z",
"_published": "2026-10-20T15:33:30.545Z",
"_upstream": "https://github.com/ctesta01/nadir",
"_status": "success",
"_maintainer": {
"name": "Christian Testa",
"email": "ctesta@hsph.harvard.edu",
"login": "ctesta01",
"mastodon": "@ctesta@fediscience.org",
"orcid": "0000-0001-9103-5839",
"description": "I'm using mathematical models to better understand how we can improve health equity. ⧉",
"uuid": 6935381
},
"_jobs": [
{
"job": 118715527119,
"time": 360,
"config": "linux-devel-x86_64",
"r": "4.7.0",
"check": "OK",
"artifact": "12277503130"
},
{
"job": 118715526639,
"time": 442,
"config": "linux-release-x86_64",
"r": "4.6.1",
"check": "OK",
"artifact": "12276629569"
},
{
"job": 118715526698,
"time": 288,
"config": "macos-oldrel-arm64",
"r": "4.5.3",
"check": "OK",
"artifact": "12276084569"
},
{
"job": 118715526692,
"time": 234,
"config": "macos-release-arm64",
"r": "4.6.1",
"check": "OK",
"artifact": "12276254362"
},
{
"job": 118711727238,
"time": 257,
"config": "pkgdown",
"r": "4.6.0",
"check": "ERROR",
"artifact": ""
},
{
"job": 118711727419,
"time": 425,
"config": "source",
"r": "4.6.1",
"check": "OK",
"artifact": "12277048339"
},
{
"job": 118715526544,
"time": 224,
"config": "wasm-release",
"r": "4.6.0",
"check": "OK",
"artifact": "12277028854"
},
{
"job": 118715526854,
"time": 417,
"config": "windows-devel-x86_64",
"r": "4.7.0",
"check": "OK",
"artifact": "12276889613"
},
{
"job": 118715526716,
"time": 414,
"config": "windows-oldrel-x86_64",
"r": "4.5.3",
"check": "OK",
"artifact": "12275949707"
},
{
"job": 118715526695,
"time": 495,
"config": "windows-release-x86_64",
"r": "4.6.1",
"check": "OK",
"artifact": "12277625858"
}
],
"_buildurl": "https://github.com/r-universe/ropensci-staging/actions/runs/39447009600",
"_commit": {
"id": "d2eef7b6ba6095107ac848842480acbd4ff37b45",
"author": "Christian Testa ",
"committer": "GitHub ",
"message": "Merge pull request #27 from ctesta01/rOpenSci_prep\n\nBugfix for non-syntactic names",
"time": 1791926689
},
"_distro": "resolute",
"_host": "GitHub-Actions",
"_registered": true,
"_dependencies": [
{
"package": "R",
"version": ">= 4.2.0",
"role": "Depends"
},
{
"package": "dplyr",
"role": "Imports"
},
{
"package": "future",
"role": "Imports"
},
{
"package": "future.apply",
"role": "Imports"
},
{
"package": "lifecycle",
"role": "Imports"
},
{
"package": "methods",
"role": "Imports"
},
{
"package": "nnls",
"role": "Imports"
},
{
"package": "origami",
"role": "Imports"
},
{
"package": "tibble",
"role": "Imports"
},
{
"package": "tidyr",
"role": "Imports"
},
{
"package": "MASS",
"role": "Suggests"
},
{
"package": "VGAM",
"role": "Suggests"
},
{
"package": "dbarts",
"role": "Suggests"
},
{
"package": "e1071",
"role": "Suggests"
},
{
"package": "earth",
"role": "Suggests"
},
{
"package": "gbm",
"role": "Suggests"
},
{
"package": "ggplot2",
"role": "Suggests"
},
{
"package": "glmnet",
"role": "Suggests"
},
{
"package": "hal9001",
"role": "Suggests"
},
{
"package": "kernlab",
"role": "Suggests"
},
{
"package": "kknn",
"role": "Suggests"
},
{
"package": "knitr",
"role": "Suggests"
},
{
"package": "lightgbm",
"role": "Suggests"
},
{
"package": "lme4",
"role": "Suggests"
},
{
"package": "mgcv",
"role": "Suggests"
},
{
"package": "nnet",
"role": "Suggests"
},
{
"package": "palmerpenguins",
"role": "Suggests"
},
{
"package": "randomForest",
"role": "Suggests"
},
{
"package": "ranger",
"role": "Suggests"
},
{
"package": "rmarkdown",
"role": "Suggests"
},
{
"package": "rpart",
"role": "Suggests"
},
{
"package": "srr",
"role": "Suggests"
},
{
"package": "survival",
"role": "Suggests"
},
{
"package": "testthat",
"version": ">= 3.0.0",
"role": "Suggests"
},
{
"package": "withr",
"role": "Suggests"
},
{
"package": "xgboost",
"role": "Suggests"
}
],
"_owner": "ctesta01",
"_selfowned": false,
"_usedby": 0,
"_updates": [
{
"week": "2025-43",
"n": 4
},
{
"week": "2026-02",
"n": 15
},
{
"week": "2026-07",
"n": 6
},
{
"week": "2026-08",
"n": 2
},
{
"week": "2026-09",
"n": 5
},
{
"week": "2026-14",
"n": 1
},
{
"week": "2026-15",
"n": 1
},
{
"week": "2026-33",
"n": 3
},
{
"week": "2026-34",
"n": 19
},
{
"week": "2026-36",
"n": 10
},
{
"week": "2026-41",
"n": 6
},
{
"week": "2026-42",
"n": 1
}
],
"_tags": [],
"_topics": [
"cross-validation",
"ensemble-learning",
"machine-learning",
"regression"
],
"_stars": 6,
"_contributors": [
{
"user": "ctesta01",
"count": 213,
"uuid": 6935381
},
{
"user": "salbalkus",
"count": 1,
"uuid": 45985925
}
],
"_userbio": {
"uuid": 318245800,
"type": "organization",
"name": "ropensci-staging",
"followers": 0
},
"_downloads": {
"count": 286,
"source": "https://cranlogs.r-pkg.org/downloads/total/last-month/nadir"
},
"_devurl": "https://github.com/ctesta01/nadir",
"_pkgdown": "https://ctesta01.github.io/nadir/",
"_searchresults": 44,
"_metadata": {
"review": {
"organization": "rOpenSci Software Review",
"url": "https://github.com/ropensci/software-review/issues/816"
}
},
"_rbuild": "4.6.1",
"_assets": [
"extra/citation.cff",
"extra/citation.html",
"extra/citation.json",
"extra/citation.txt",
"extra/contents.json",
"extra/manual.pdf",
"extra/nadir.html",
"extra/NEWS.html",
"extra/NEWS.txt",
"extra/readme.html",
"extra/readme.md",
"LICENSE"
],
"_homeurl": "https://github.com/ctesta01/nadir",
"_realowner": "ctesta01",
"_cranurl": true,
"_releases": [
{
"version": "0.0.1",
"date": "2026-02-20"
}
],
"_exports": [
"add_screener",
"add_stratification",
"as_multi_predictor",
"compare_learners",
"crossfit_super_learner",
"cv_character_and_factors_schema",
"cv_origami_schema",
"cv_random_schema",
"cv_super_learner",
"default_determine_weights",
"default_loss_metric",
"determine_super_learner_weights_nnls",
"determine_weights_for_binary_outcomes",
"determine_weights_using_neg_log_loss",
"df_to_survival_stacked",
"list_known_learners",
"lnr_bart",
"lnr_cvglmnet",
"lnr_earth",
"lnr_gam",
"lnr_gausspr",
"lnr_gbm",
"lnr_glm",
"lnr_glm_density",
"lnr_glmer",
"lnr_glmnet",
"lnr_glmnet_grid",
"lnr_hal",
"lnr_hal_grid",
"lnr_heteroskedastic_density",
"lnr_homoskedastic_density",
"lnr_knn",
"lnr_knn_binary",
"lnr_lightgbm",
"lnr_lm",
"lnr_lm_density",
"lnr_lmer",
"lnr_logistic",
"lnr_mean",
"lnr_multinomial_nnet",
"lnr_multinomial_ranger",
"lnr_multinomial_vglm",
"lnr_nnet",
"lnr_ppr",
"lnr_ranger",
"lnr_ranger_binary",
"lnr_rf",
"lnr_rf_binary",
"lnr_rpart",
"lnr_rpart_binary",
"lnr_svm",
"lnr_svm_binary",
"lnr_xgboost",
"make_learner_names_unique",
"nadir_supported_types",
"negative_log_loss",
"negative_log_loss_for_binary",
"screener_cor",
"screener_cor_top_n",
"screener_t_test",
"super_learner",
"truncate_lnr"
],
"_help": [
{
"page": "add_screener",
"title": "Add a Screener to a Learner",
"topics": [
"add_screener"
]
},
{
"page": "add_stratification",
"title": "Stratify a Learner by One or More Categorical Variables",
"topics": [
"add_stratification"
]
},
{
"page": "as_multi_predictor",
"title": "Construct a Multi-Predictor",
"topics": [
"as_multi_predictor"
]
},
{
"page": "binary_learners",
"title": "Binary Learners in '{nadir}'",
"topics": [
"binary_learners"
]
},
{
"page": "compare_learners",
"title": "Compare Learners",
"topics": [
"compare_learners"
]
},
{
"page": "crossfit_sl_methods",
"title": "Predicting from a Cross-Fitted Super Learner",
"topics": [
"coef.nadir_crossfit_sl",
"crossfit_sl_methods",
"fitted.nadir_crossfit_sl",
"formula.nadir_crossfit_sl",
"nobs.nadir_crossfit_sl",
"plot.nadir_crossfit_sl",
"predict.nadir_crossfit_sl",
"print.summary.nadir_crossfit_sl",
"residuals.nadir_crossfit_sl",
"summary.nadir_crossfit_sl"
]
},
{
"page": "crossfit_super_learner",
"title": "Cross-Fit a Super Learner and Retain Fold-Specific Predictors",
"topics": [
"crossfit_super_learner"
]
},
{
"page": "cv_character_and_factors_schema",
"title": "Cross Validation Training/Validation Splits with Characters/Factor Columns",
"topics": [
"cv_character_and_factors_schema"
]
},
{
"page": "cv_origami_schema",
"title": "Cross-Validation with Origami",
"topics": [
"cv_origami_schema"
]
},
{
"page": "cv_random_schema",
"title": "Assign Data to One of n_folds Randomly and Produce Training/Validation Data Lists",
"topics": [
"cv_random_schema"
]
},
{
"page": "cv_super_learner",
"title": "Cross-Validating a 'super_learner'",
"topics": [
"cv_super_learner"
]
},
{
"page": "density_learners",
"title": "Conditional Density Estimation in the '{nadir}' Package",
"topics": [
"density_learners"
]
},
{
"page": "determine_super_learner_weights_nnls",
"title": "Determine SuperLearner Weights with Nonnegative Least Squares",
"topics": [
"determine_super_learner_weights_nnls"
]
},
{
"page": "determine_weights_for_binary_outcomes",
"title": "Determine Weights Appropriately for Super Learner given Binary Outcomes",
"topics": [
"determine_weights_for_binary_outcomes"
]
},
{
"page": "determine_weights_using_neg_log_loss",
"title": "Determine Weights for Density Estimators for SuperLearner",
"topics": [
"determine_weights_using_neg_log_loss"
]
},
{
"page": "df_to_survival_stacked",
"title": "Repeat Observations for Survival Stacking",
"topics": [
"df_to_survival_stacked"
]
},
{
"page": "learners",
"title": "Learners in the '{nadir}' Package",
"topics": [
"learners"
]
},
{
"page": "list_known_learners",
"title": "List Known Learners",
"topics": [
"list_known_learners"
]
},
{
"page": "lnr_bart",
"title": "Bayesian Additive Regression Trees (BART) Learner",
"topics": [
"lnr_bart"
]
},
{
"page": "lnr_cvglmnet",
"title": "cv.glmnet Learner",
"topics": [
"lnr_cvglmnet"
]
},
{
"page": "lnr_earth",
"title": "Earth Learner",
"topics": [
"lnr_earth"
]
},
{
"page": "lnr_gam",
"title": "Generalized Additive Model Learner",
"topics": [
"lnr_gam"
]
},
{
"page": "lnr_gausspr",
"title": "Gaussian Process Regression Learner",
"topics": [
"lnr_gausspr"
]
},
{
"page": "lnr_gbm",
"title": "Gradient Boosting Machines Learner",
"topics": [
"lnr_gbm"
]
},
{
"page": "lnr_glm",
"title": "GLM Learner",
"topics": [
"lnr_glm"
]
},
{
"page": "lnr_glm_density",
"title": "Conditional Normal Density Estimation Given Mean Predictors — with GLMs",
"topics": [
"lnr_glm_density"
]
},
{
"page": "lnr_glmer",
"title": "Generalized Linear Mixed-Effects ('lme4::glmer') Learner",
"topics": [
"lnr_glmer"
]
},
{
"page": "lnr_glmnet",
"title": "glmnet Learner",
"topics": [
"lnr_glmnet"
]
},
{
"page": "lnr_glmnet_grid",
"title": "glmnet Learner over a Grid of Lambda Values",
"topics": [
"lnr_glmnet_grid"
]
},
{
"page": "lnr_hal",
"title": "Highly Adaptive Lasso",
"topics": [
"lnr_hal"
]
},
{
"page": "lnr_hal_grid",
"title": "Highly Adaptive Lasso over a Grid of Lambda Values",
"topics": [
"lnr_hal_grid"
]
},
{
"page": "lnr_heteroskedastic_density",
"title": "Conditional Density Estimation with Heteroskedasticity",
"topics": [
"lnr_heteroskedastic_density"
]
},
{
"page": "lnr_homoskedastic_density",
"title": "Conditional Density Estimation with Homoskedasticity Assumption",
"topics": [
"lnr_homoskedastic_density"
]
},
{
"page": "lnr_knn",
"title": "k-Nearest Neighbors Learner",
"topics": [
"lnr_knn"
]
},
{
"page": "lnr_knn_binary",
"title": "k-Nearest Neighbors Learner for Binary Classification",
"topics": [
"lnr_knn_binary"
]
},
{
"page": "lnr_lightgbm",
"title": "LightGBM Learner",
"topics": [
"lnr_lightgbm"
]
},
{
"page": "lnr_lm",
"title": "Linear Model Learner",
"topics": [
"lnr_lm"
]
},
{
"page": "lnr_lm_density",
"title": "Conditional Normal Density Estimation Given Mean Predictors",
"topics": [
"lnr_lm_density"
]
},
{
"page": "lnr_lmer",
"title": "Random/Mixed-Effects ('lme4::lmer') Learner",
"topics": [
"lnr_lmer"
]
},
{
"page": "lnr_logistic",
"title": "Standard Logistic Regression for Binary Classification",
"topics": [
"lnr_logistic"
]
},
{
"page": "lnr_mean",
"title": "Mean Learner",
"topics": [
"lnr_mean"
]
},
{
"page": "lnr_multinomial_nnet",
"title": "'nnet::multinom' Multinomial Learner",
"topics": [
"lnr_multinomial_nnet"
]
},
{
"page": "lnr_multinomial_ranger",
"title": "ranger Multinomial Learner",
"topics": [
"lnr_multinomial_ranger"
]
},
{
"page": "lnr_multinomial_vglm",
"title": "'VGAM::vglm' Multinomial Learner",
"topics": [
"lnr_multinomial_vglm"
]
},
{
"page": "lnr_nnet",
"title": "Use nnet for Binary Classification",
"topics": [
"lnr_nnet"
]
},
{
"page": "lnr_ppr",
"title": "Projection Pursuit Regression Learner",
"topics": [
"lnr_ppr"
]
},
{
"page": "lnr_ranger",
"title": "ranger Learner",
"topics": [
"lnr_ranger"
]
},
{
"page": "lnr_ranger_binary",
"title": "ranger Learner for Binary Outcomes",
"topics": [
"lnr_ranger_binary"
]
},
{
"page": "lnr_rf",
"title": "randomForest Learner",
"topics": [
"lnr_rf"
]
},
{
"page": "lnr_rf_binary",
"title": "Use Random Forest for Binary Classification",
"topics": [
"lnr_rf_binary"
]
},
{
"page": "lnr_rpart",
"title": "Recursive Partitioning (CART) Learner",
"topics": [
"lnr_rpart"
]
},
{
"page": "lnr_rpart_binary",
"title": "Recursive Partitioning (CART) Learner for Binary Classification",
"topics": [
"lnr_rpart_binary"
]
},
{
"page": "lnr_svm",
"title": "Support Vector Machine Learner",
"topics": [
"lnr_svm"
]
},
{
"page": "lnr_svm_binary",
"title": "Support Vector Machine Learner for Binary Classification",
"topics": [
"lnr_svm_binary"
]
},
{
"page": "lnr_xgboost",
"title": "XGBoost Learner",
"topics": [
"lnr_xgboost"
]
},
{
"page": "make_learner_names_unique",
"title": "Make Unique Learner Names",
"topics": [
"make_learner_names_unique"
]
},
{
"page": "multiclass_learners",
"title": "Multiclass Learners in '{nadir}'",
"topics": [
"multiclass_learners"
]
},
{
"page": "nadir_sl_model_methods",
"title": "Print a 'nadir_sl_model'",
"topics": [
"coef.nadir_sl_model",
"fitted.nadir_sl_model",
"formula.nadir_sl_model",
"nadir_sl_model_methods",
"nobs.nadir_sl_model",
"predict.nadir_sl_model",
"print.nadir_sl_model",
"residuals.nadir_sl_model",
"summary.nadir_sl_model"
]
},
{
"page": "nadir_supported_types",
"title": "Outcome types supported by '{nadir}'",
"topics": [
"nadir_supported_types"
]
},
{
"page": "negative_log_loss",
"title": "Negative Log Loss",
"topics": [
"negative_log_loss"
]
},
{
"page": "negative_log_loss_for_binary",
"title": "Negative Log Loss for Binary",
"topics": [
"negative_log_loss_for_binary"
]
},
{
"page": "cv_sl_methods",
"title": "Plot a Cross-Validated Super Learner",
"topics": [
"plot.nadir_cv_sl",
"print.nadir_cv_sl"
]
},
{
"page": "plot.nadir_sl_model",
"title": "Plot a 'nadir_sl_model'",
"topics": [
"plot.nadir_sl_model"
]
},
{
"page": "screener_cor",
"title": "Correlation Threshold Based Screening",
"topics": [
"screener_cor"
]
},
{
"page": "screener_cor_top_n",
"title": "Correlation Threshold Based Screening",
"topics": [
"screener_cor_top_n"
]
},
{
"page": "screener_t_test",
"title": "t-test Based Screening: Thresholds on p.values and/or t statistics",
"topics": [
"screener_t_test"
]
},
{
"page": "screeners",
"title": "Wrapping Learners with a Screener",
"topics": [
"screeners"
]
},
{
"page": "super_learner",
"title": "Super Learner: Cross-Validation Based Ensemble Learning",
"topics": [
"super_learner"
]
},
{
"page": "truncate_lnr",
"title": "Helper to Truncate a Learner's Predictions",
"topics": [
"truncate_lnr"
]
}
],
"_pkglogo": "https://github.com/ctesta01/nadir/raw/HEAD/man/figures/logo.png",
"_readme": "https://github.com/ctesta01/nadir/raw/HEAD/README.md",
"_rundeps": [
"abind",
"assertthat",
"cli",
"codetools",
"cpp11",
"data.table",
"digest",
"dplyr",
"future",
"future.apply",
"generics",
"globals",
"glue",
"lifecycle",
"listenv",
"magrittr",
"nnls",
"origami",
"parallelly",
"pillar",
"pkgconfig",
"purrr",
"R6",
"rlang",
"stringi",
"stringr",
"tibble",
"tidyr",
"tidyselect",
"utf8",
"vctrs",
"withr"
],
"_vignettes": [
{
"source": "Basic-Examples.Rmd",
"filename": "Basic-Examples.html",
"title": "Basic Examples",
"engine": "knitr::rmarkdown",
"headings": [
"Getting More Information Out"
],
"created": "2026-01-05 17:03:46",
"modified": "2026-10-10 17:17:29",
"commits": 3
}
],
"_score": 6.199755177253475,
"_indexed": false,
"_nocasepkg": "nadir",
"_universes": [
"ropensci-staging"
],
"_indexurl": "https://ctesta01.r-universe.dev/nadir",
"_binaries": [
{
"r": "4.7.0",
"os": "linux",
"version": "1.0.0",
"date": "2026-10-20T15:27:29.000Z",
"distro": "resolute",
"commit": "d2eef7b6ba6095107ac848842480acbd4ff37b45",
"fileid": "https://r2.ropensci.org/aad420b3985dc8ac6259c88fe793ca96fc9e2d037602f443cbada7f444268c28",
"status": "success",
"check": "OK",
"buildurl": "https://github.com/r-universe/ropensci-staging/actions/runs/39447009600"
},
{
"r": "4.6.1",
"os": "linux",
"version": "1.0.0",
"date": "2026-10-20T15:28:54.000Z",
"distro": "resolute",
"commit": "d2eef7b6ba6095107ac848842480acbd4ff37b45",
"fileid": "https://r2.ropensci.org/7f4b82d8cc23de8368fe588e0130773e57c0ad3c462920d1d5f79c157a71241e",
"status": "success",
"check": "OK",
"buildurl": "https://github.com/r-universe/ropensci-staging/actions/runs/39447009600"
},
{
"r": "4.5.3",
"os": "mac",
"version": "1.0.0",
"date": "2026-10-20T15:28:24.000Z",
"commit": "d2eef7b6ba6095107ac848842480acbd4ff37b45",
"fileid": "https://r2.ropensci.org/2d5ba185dcdfdb067e2bb94afca040d724392eff24d8d43403bdeb7c9ce788ec",
"status": "success",
"check": "OK",
"buildurl": "https://github.com/r-universe/ropensci-staging/actions/runs/39447009600"
},
{
"r": "4.6.1",
"os": "mac",
"version": "1.0.0",
"date": "2026-10-20T15:28:43.000Z",
"commit": "d2eef7b6ba6095107ac848842480acbd4ff37b45",
"fileid": "https://r2.ropensci.org/e0d260e7e1d539125129fdd5570ea265457e93f2b946923c1ec53197445c0b3b",
"status": "success",
"check": "OK",
"buildurl": "https://github.com/r-universe/ropensci-staging/actions/runs/39447009600"
},
{
"r": "4.6.0",
"os": "wasm",
"version": "1.0.0",
"date": "2026-10-20T15:28:34.000Z",
"commit": "d2eef7b6ba6095107ac848842480acbd4ff37b45",
"fileid": "https://r2.ropensci.org/31ecd10022e17392969257dc5730cefef8f02dba8ddd3418f8e51670d4695747",
"status": "success",
"buildurl": "https://github.com/r-universe/ropensci-staging/actions/runs/39447009600"
},
{
"r": "4.7.0",
"os": "win",
"version": "1.0.0",
"date": "2026-10-20T15:25:53.000Z",
"commit": "d2eef7b6ba6095107ac848842480acbd4ff37b45",
"fileid": "https://r2.ropensci.org/68110bf626586756409f9ede6856f8710c92a4a56de487b1cf3826dbc3a50396",
"status": "success",
"check": "OK",
"buildurl": "https://github.com/r-universe/ropensci-staging/actions/runs/39447009600"
},
{
"r": "4.5.3",
"os": "win",
"version": "1.0.0",
"date": "2026-10-20T15:26:02.000Z",
"commit": "d2eef7b6ba6095107ac848842480acbd4ff37b45",
"fileid": "https://r2.ropensci.org/84cc4b0448d576fa75746f8e6254e504262575d277ac434ef8dc3ce39946a102",
"status": "success",
"check": "OK",
"buildurl": "https://github.com/r-universe/ropensci-staging/actions/runs/39447009600"
},
{
"r": "4.6.1",
"os": "win",
"version": "1.0.0",
"date": "2026-10-20T15:27:01.000Z",
"commit": "d2eef7b6ba6095107ac848842480acbd4ff37b45",
"fileid": "https://r2.ropensci.org/aba6d67acfffe9393910ebc988c10585f84221be2ffd043ab09677ff11ad0671",
"status": "success",
"check": "OK",
"buildurl": "https://github.com/r-universe/ropensci-staging/actions/runs/39447009600"
}
]
}