{ "_id": "6ad07acfe2d863f662954e1e", "Package": "vimp", "Type": "Package", "Title": "Perform Inference on Algorithm-Agnostic Variable Importance", "Version": "2.3.8", "Authors@R": "c(person(given = \"Brian D.\",\nfamily = \"Williamson\",\nrole = c(\"aut\", \"cre\"),\nemail = \"brian.d.williamson@kp.org\",\ncomment = c(ORCID = \"0000-0002-7024-548X\")),\nperson(given = \"Jean\",\nfamily = \"Feng\",\nrole = \"ctb\"),\nperson(given = \"Charlie\",\nfamily = \"Wolock\",\nrole = \"ctb\"),\nperson(given = \"Noah\",\nfamily = \"Simon\",\nrole = \"ths\",\ncomment = c(ORCID = \"0000-0002-8985-2474\")),\nperson(given = \"Marco\",\nfamily = \"Carone\",\nrole = \"ths\",\ncomment = c(ORCID = \"0000-0003-2106-0953\")))", "Description": "Calculate point estimates of and valid confidence\nintervals for nonparametric, algorithm-agnostic variable\nimportance measures in high and low dimensions, using flexible\nestimators of the underlying regression functions. For more\ninformation about the methods, please see Williamson et al.\n(Biometrics, 2020), Williamson et al. (JASA, 2021), and\nWilliamson and Feng (ICML, 2020).", "License": "MIT + file LICENSE", "URL": "https://bdwilliamson.github.io/vimp/,\nhttps://github.com/bdwilliamson/vimp,\nhttp://bdwilliamson.github.io/vimp/", "BugReports": "https://github.com/bdwilliamson/vimp/issues", "RoxygenNote": "7.3.2", "VignetteBuilder": "knitr", "LazyData": "true", "Config/pak/sysreqs": "libicu-dev", "Repository": "https://bdwilliamson.r-universe.dev", "Date/Publication": "2026-05-18 16:57:01 UTC", "RemoteUrl": "https://github.com/bdwilliamson/vimp", "RemoteRef": "HEAD", "RemoteSha": "c160a9fe05dbe78c8e31bb9d3f89e8be32509924", "NeedsCompilation": "no", "Packaged": { "Date": "2026-10-15 06:39:12 UTC", "User": "root" }, "Author": "Brian D. Williamson [aut, cre] (ORCID:\n),\nJean Feng [ctb],\nCharlie Wolock [ctb],\nNoah Simon [ths] (ORCID: ),\nMarco Carone [ths] (ORCID: )", "Maintainer": "Brian D. Williamson ", "_user": "bdwilliamson", "_type": "src", "_file": "vimp_2.3.8.tar.gz", "_fileid": "https://r2.ropensci.org/48c54ecb2175ac991a33ff1ddf42843e7c8a3b0a7cc9419527c8706492d94dc4", "_filesize": 1174085, "_sha256": "48c54ecb2175ac991a33ff1ddf42843e7c8a3b0a7cc9419527c8706492d94dc4", "_expires": "2027-01-23T07:03:41.000Z", "_created": "2026-10-15T06:39:12.000Z", "_published": "2026-10-15T07:03:43.814Z", "_buildurl": "https://github.com/r-universe/bdwilliamson/actions/runs/38693239223", "_status": "success", "_host": "GitHub-Actions", "_jobs": [ { "job": 116285370408, "time": 169, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "NOTE", "artifact": "11953730350" }, { "job": 116285370548, "time": 137, "config": "linux-release-x86_64", "r": "4.6.1", "check": "NOTE", "artifact": "11953192166" }, { "job": 116285370381, "time": 104, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "NOTE", "artifact": "11953184956" }, { "job": 116285370522, "time": 133, "config": "macos-release-arm64", "r": "4.6.1", "check": "NOTE", "artifact": "11953869414" }, { "job": 116283882410, "time": 274, "config": "source", "r": "4.6.1", "check": "OK", "artifact": "11952664592" }, { "job": 116285370345, "time": 111, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "11953750117" }, { "job": 116285370412, "time": 135, "config": "windows-devel-x86_64", "r": "4.7.0", "check": "NOTE", "artifact": "11953850110" }, { "job": 116285370585, "time": 166, "config": "windows-oldrel-x86_64", "r": "4.5.3", "check": "NOTE", "artifact": "11953990076" }, { "job": 116285370449, "time": 128, "config": "windows-release-x86_64", "r": "4.6.1", "check": "NOTE", "artifact": "11953172127" } ], "_upstream": "https://github.com/bdwilliamson/vimp", "_commit": { "id": "c160a9fe05dbe78c8e31bb9d3f89e8be32509924", "author": "Brian Williamson ", "committer": "Brian Williamson ", "message": "Add rsample to imports\n", "time": 1779123421 }, "_maintainer": { "name": "Brian D. Williamson", "email": "brian.d.williamson@kp.org", "login": "bdwilliamson", "description": "Associate Investigator at Kaiser Permanente Washington Health Research Institute. Interested in inference in high-dimensional settings.", "uuid": 13205756, "orcid": "0000-0002-7024-548X" }, "_distro": "resolute", "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 3.1.0", "role": "Depends" }, { "package": "SuperLearner", "role": "Imports" }, { "package": "stats", "role": "Imports" }, { "package": "dplyr", "role": "Imports" }, { "package": "magrittr", "role": "Imports" }, { "package": "ROCR", "role": "Imports" }, { "package": "tibble", "role": "Imports" }, { "package": "rlang", "role": "Imports" }, { "package": "MASS", "role": "Imports" }, { "package": "data.table", "role": "Imports" }, { "package": "boot", "role": "Imports" }, { "package": "rsample", "role": "Imports" }, { "package": "knitr", "role": "Suggests" }, { "package": "rmarkdown", "role": "Suggests" }, { "package": "gam", "role": "Suggests" }, { "package": "xgboost", "role": "Suggests" }, { "package": "glmnet", "role": "Suggests" }, { "package": "ranger", "role": "Suggests" }, { "package": "polspline", "role": "Suggests" }, { "package": "quadprog", "role": "Suggests" }, { "package": "covr", "role": "Suggests" }, { "package": "testthat", "role": "Suggests" }, { "package": "ggplot2", "role": "Suggests" }, { "package": "cowplot", "role": "Suggests" }, { "package": "cvAUC", "role": "Suggests" }, { "package": "tidyselect", "role": "Suggests" }, { "package": "WeightedROC", "role": "Suggests" }, { "package": "purrr", "role": "Suggests" } ], "_owner": "bdwilliamson", "_selfowned": true, "_usedby": 1, "_updates": [ { "week": "2026-20", "n": 4 }, { "week": "2026-21", "n": 1 } ], "_tags": [], "_topics": [ "machine-learning", "nonparametric-statistics", "statistical-inference", "variable-importance" ], "_stars": 24, "_contributors": [ { "user": "bdwilliamson", "count": 1037, "uuid": 13205756 }, { "user": "cwolock", "count": 8, "uuid": 24723547 }, { "user": "jjfeng", "count": 4, "uuid": 528599 } ], "_userbio": { "uuid": 13205756, "type": "user", "name": "Brian Williamson", "followers": 43, "description": "Associate Investigator at Kaiser Permanente Washington Health Research Institute. Interested in inference in high-dimensional settings." }, "_downloads": { "count": 639, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/vimp" }, "_mentions": 1, "_devurl": "https://github.com/bdwilliamson/vimp", "_pkgdown": "https://bdwilliamson.github.io/vimp/", "_searchresults": 60, "_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/vimp.html", "LICENSE" ], "_homeurl": "https://github.com/bdwilliamson/vimp", "_realowner": "bdwilliamson", "_cranurl": true, "_releases": [ { "version": "1.0.0", "date": "2018-06-24" }, { "version": "1.1.0", "date": "2018-08-09" }, { "version": "1.1.2", "date": "2018-09-20" }, { "version": "1.1.3", "date": "2018-10-02" }, { "version": "1.1.4", "date": "2018-10-14" }, { "version": "1.1.5", "date": "2019-08-09" }, { "version": "1.1.6", "date": "2019-08-27" }, { "version": "2.0.1", "date": "2020-04-12" }, { "version": "2.0.2", "date": "2020-04-27" }, { "version": "2.1.0", "date": "2020-06-18" }, { "version": "2.1.6", "date": "2021-01-09" }, { "version": "2.1.9", "date": "2021-03-01" }, { "version": "2.2.1", "date": "2021-06-03" }, { "version": "2.2.2", "date": "2021-06-14" }, { "version": "2.2.3", "date": "2021-07-20" }, { "version": "2.2.4", "date": "2021-08-04" }, { "version": "2.2.5", "date": "2021-08-16" }, { "version": "2.3.0", "date": "2022-11-14" }, { "version": "2.3.1", "date": "2022-12-09" }, { "version": "2.3.3", "date": "2023-08-28" }, { "version": "2.3.5", "date": "2025-07-23" }, { "version": "2.3.6", "date": "2025-08-28" } ], "_exports": [ "average_vim", "bootstrap_se", "check_fitted_values", "check_inputs", "create_z", "cv_vim", "est_predictiveness", "est_predictiveness_cv", "estimate", "estimate_nuisances", "extract_sampled_split_predictions", "get_cv_sl_folds", "get_full_type", "get_test_set", "make_folds", "make_kfold", "measure_accuracy", "measure_anova", "measure_auc", "measure_average_value", "measure_cross_entropy", "measure_deviance", "measure_mse", "measure_npv", "measure_ppv", "measure_r_squared", "measure_sensitivity", "measure_specificity", "merge_vim", "predictiveness_measure", "process_arg_lst", "run_sl", "sample_subsets", "scale_est", "sp_vim", "spvim_ics", "spvim_se", "vim", "vimp_accuracy", "vimp_anova", "vimp_auc", "vimp_ci", "vimp_deviance", "vimp_hypothesis_test", "vimp_regression", "vimp_rsquared", "vimp_se" ], "_datasets": [ { "name": "vrc01", "title": "Neutralization sensitivity of HIV viruses to antibody VRC01", "object": "vrc01", "class": [ "data.frame" ], "fields": [], "rows": 611, "table": true, "tojson": true } ], "_help": [ { "page": "average_vim", "title": "Average multiple independent importance estimates", "topics": [ "average_vim" ] }, { "page": "bootstrap_se", "title": "Compute bootstrap-based standard error estimates for variable importance", "topics": [ "bootstrap_se" ] }, { "page": "check_fitted_values", "title": "Check pre-computed fitted values for call to vim, cv_vim, or sp_vim", "topics": [ "check_fitted_values" ] }, { "page": "check_inputs", "title": "Check inputs to a call to vim, cv_vim, or sp_vim", "topics": [ "check_inputs" ] }, { "page": "create_z", "title": "Create complete-case outcome, weights, and Z", "topics": [ "create_z" ] }, { "page": "cv_vim", "title": "Nonparametric Intrinsic Variable Importance Estimates and Inference using Cross-fitting", "topics": [ "cv_vim" ] }, { "page": "est_predictiveness", "title": "Estimate a nonparametric predictiveness functional", "topics": [ "est_predictiveness" ] }, { "page": "est_predictiveness_cv", "title": "Estimate a nonparametric predictiveness functional using cross-fitting", "topics": [ "est_predictiveness_cv" ] }, { "page": "estimate", "title": "Estimate a Predictiveness Measure", "topics": [ "estimate" ] }, { "page": "estimate_eif_projection", "title": "Estimate projection of EIF on fully-observed variables", "topics": [ "estimate_eif_projection" ] }, { "page": "estimate_nuisances", "title": "Estimate nuisance functions for average value-based VIMs", "topics": [ "estimate_nuisances" ] }, { "page": "estimate_type_predictiveness", "title": "Estimate Predictiveness Given a Type", "topics": [ "estimate_type_predictiveness" ] }, { "page": "estimate.predictiveness_measure", "title": "Obtain a Point Estimate and Efficient Influence Function Estimate for a Given Predictiveness Measure", "topics": [ "estimate.predictiveness_measure" ] }, { "page": "extract_sampled_split_predictions", "title": "Extract sampled-split predictions from a CV.SuperLearner object", "topics": [ "extract_sampled_split_predictions" ] }, { "page": "format.predictiveness_measure", "title": "Format a 'predictiveness_measure' object", "topics": [ "format.predictiveness_measure" ] }, { "page": "format.vim", "title": "Format a 'vim' object", "topics": [ "format.vim" ] }, { "page": "get_cv_sl_folds", "title": "Get a numeric vector with cross-validation fold IDs from CV.SuperLearner", "topics": [ "get_cv_sl_folds" ] }, { "page": "get_full_type", "title": "Obtain the type of VIM to estimate using partial matching", "topics": [ "get_full_type" ] }, { "page": "get_test_set", "title": "Return test-set only data", "topics": [ "get_test_set" ] }, { "page": "make_folds", "title": "Create Folds for Cross-Fitting", "topics": [ "make_folds" ] }, { "page": "make_kfold", "title": "Turn folds from 2K-fold cross-fitting into individual K-fold folds", "topics": [ "make_kfold" ] }, { "page": "measure_accuracy", "title": "Estimate the classification accuracy", "topics": [ "measure_accuracy" ] }, { "page": "measure_anova", "title": "Estimate ANOVA decomposition-based variable importance.", "topics": [ "measure_anova" ] }, { "page": "measure_auc", "title": "Estimate area under the receiver operating characteristic curve (AUC)", "topics": [ "measure_auc" ] }, { "page": "measure_average_value", "title": "Estimate the average value under the optimal treatment rule", "topics": [ "measure_average_value" ] }, { "page": "measure_cross_entropy", "title": "Estimate the cross-entropy", "topics": [ "measure_cross_entropy" ] }, { "page": "measure_deviance", "title": "Estimate the deviance", "topics": [ "measure_deviance" ] }, { "page": "measure_mse", "title": "Estimate mean squared error", "topics": [ "measure_mse" ] }, { "page": "measure_npv", "title": "Estimate the positive predictive value (NPV)", "topics": [ "measure_npv" ] }, { "page": "measure_ppv", "title": "Estimate the positive predictive value (PPV)", "topics": [ "measure_ppv" ] }, { "page": "measure_r_squared", "title": "Estimate R-squared", "topics": [ "measure_r_squared" ] }, { "page": "measure_sensitivity", "title": "Estimate the sensitivity", "topics": [ "measure_sensitivity" ] }, { "page": "measure_specificity", "title": "Estimate the specificity", "topics": [ "measure_specificity" ] }, { "page": "merge_vim", "title": "Merge multiple 'vim' objects into one", "topics": [ "merge_vim" ] }, { "page": "predictiveness_measure", "title": "Construct a Predictiveness Measure", "topics": [ "predictiveness_measure" ] }, { "page": "print.predictiveness_measure", "title": "Print 'predictiveness_measure' objects", "topics": [ "print.predictiveness_measure" ] }, { "page": "print.vim", "title": "Print 'vim' objects", "topics": [ "print.vim" ] }, { "page": "process_arg_lst", "title": "Process argument list for Super Learner estimation of the EIF", "topics": [ "process_arg_lst" ] }, { "page": "run_sl", "title": "Run a Super Learner for the provided subset of features", "topics": [ "run_sl" ] }, { "page": "sample_subsets", "title": "Create necessary objects for SPVIMs", "topics": [ "sample_subsets" ] }, { "page": "scale_est", "title": "Return an estimator on a different scale", "topics": [ "scale_est" ] }, { "page": "sp_vim", "title": "Shapley Population Variable Importance Measure (SPVIM) Estimates and Inference", "topics": [ "sp_vim" ] }, { "page": "spvim_ics", "title": "Influence function estimates for SPVIMs", "topics": [ "spvim_ics" ] }, { "page": "spvim_se", "title": "Standard error estimate for SPVIM values", "topics": [ "spvim_se" ] }, { "page": "vim", "title": "Nonparametric Intrinsic Variable Importance Estimates and Inference", "topics": [ "vim" ] }, { "page": "vimp_accuracy", "title": "Nonparametric Intrinsic Variable Importance Estimates: Classification accuracy", "topics": [ "vimp_accuracy" ] }, { "page": "vimp_anova", "title": "Nonparametric Intrinsic Variable Importance Estimates: ANOVA", "topics": [ "vimp_anova" ] }, { "page": "vimp_auc", "title": "Nonparametric Intrinsic Variable Importance Estimates: AUC", "topics": [ "vimp_auc" ] }, { "page": "vimp_ci", "title": "Confidence intervals for variable importance", "topics": [ "vimp_ci" ] }, { "page": "vimp_deviance", "title": "Nonparametric Intrinsic Variable Importance Estimates: Deviance", "topics": [ "vimp_deviance" ] }, { "page": "vimp_hypothesis_test", "title": "Perform a hypothesis test against the null hypothesis of delta importance", "topics": [ "vimp_hypothesis_test" ] }, { "page": "vimp_regression", "title": "Nonparametric Intrinsic Variable Importance Estimates: ANOVA", "topics": [ "vimp_regression" ] }, { "page": "vimp_rsquared", "title": "Nonparametric Intrinsic Variable Importance Estimates: R-squared", "topics": [ "vimp_rsquared" ] }, { "page": "vimp_se", "title": "Estimate variable importance standard errors", "topics": [ "vimp_se" ] }, { "page": "vrc01", "title": "Neutralization sensitivity of HIV viruses to antibody VRC01", "topics": [ "vrc01" ] } ], "_pkglogo": "https://github.com/bdwilliamson/vimp/raw/HEAD/man/figures/logo.png", "_readme": "https://github.com/bdwilliamson/vimp/raw/HEAD/README.md", "_rundeps": [ "bitops", "boot", "caTools", "cli", "codetools", "cpp11", "cvAUC", "data.table", "digest", "dplyr", "foreach", "furrr", "future", "gam", "generics", "globals", "glue", "gplots", "gtools", "iterators", "KernSmooth", "lifecycle", "listenv", "magrittr", "MASS", "nnls", "parallelly", "pillar", "pkgconfig", "purrr", "R6", "rlang", "ROCR", "rsample", "slider", "stringi", "stringr", "SuperLearner", "tibble", "tidyr", "tidyselect", "utf8", "vctrs", "warp", "withr" ], "_vignettes": [ { "source": "introduction-to-vimp.Rmd", "filename": "introduction-to-vimp.html", "title": "Introduction to vimp", "author": "Brian D. Williamson", "engine": "knitr::rmarkdown", "headings": [ "Introduction", "Installation", "Quick Start", "Detailed guide", "A look at the VRC01 data", "A first approach: linear regression", "Building a library of learners", "Estimating variable importance for a single variable", "Estimating variable importance for a group of variables", "Types of population variable importance", "References" ], "created": "2020-06-23 23:51:53", "modified": "2025-02-12 16:31:39", "commits": 15 }, { "source": "types-of-vims.Rmd", "filename": "types-of-vims.html", "title": "Types of VIMs", "author": "Brian D. Williamson", "engine": "knitr::rmarkdown", "headings": [ "Introduction", "Conditional VIMs", "Marginal VIMs", "Shapley VIMs", "Adjusting for confounders", "Conclusion", "References" ], "created": "2020-06-23 23:51:53", "modified": "2021-08-03 23:46:02", "commits": 12 }, { "source": "precomputed-regressions.Rmd", "filename": "precomputed-regressions.html", "title": "Using precomputed regression function estimates in vimp", "author": "Brian D. Williamson", "engine": "knitr::rmarkdown", "headings": [ "Introduction", "Using precomputed regression function estimates without cross-fitting", "A first approach: linear regression", "Estimating variable importance for a single variable using precomputed regression function estimates", "Estimating variable importance for a group of variables using precomputed regression function estimates", "Conclusion", "References" ], "created": "2020-06-23 23:51:53", "modified": "2025-02-12 16:31:39", "commits": 21 }, { "source": "ipcw-vim.Rmd", "filename": "ipcw-vim.html", "title": "Variable importance with coarsened data", "author": "Brian D. Williamson", "engine": "knitr::rmarkdown", "headings": [ "Introduction", "Coarsened data in vimp", "Example with missing outcomes", "Example with two-phase sampling", "References" ], "created": "2022-03-31 17:55:29", "modified": "2025-02-12 16:31:39", "commits": 4 } ], "_score": 7.538573733806856, "_indexed": true, "_nocasepkg": "vimp", "_universes": [ "bdwilliamson" ], "_binaries": [ { "r": "4.7.0", "os": "linux", "version": "2.3.8", "date": "2026-10-15T06:41:31.000Z", "distro": "resolute", "commit": "c160a9fe05dbe78c8e31bb9d3f89e8be32509924", "fileid": "https://r2.ropensci.org/c854b54c3561765870257cfb5403045d0035bc719789b4cd5523c6fddb51cd04", "status": "success", "check": "NOTE", "buildurl": "https://github.com/r-universe/bdwilliamson/actions/runs/38693239223" }, { "r": "4.6.1", "os": "linux", "version": "2.3.8", "date": "2026-10-15T06:41:19.000Z", "distro": "resolute", "commit": "c160a9fe05dbe78c8e31bb9d3f89e8be32509924", "fileid": "https://r2.ropensci.org/fd28053e4f4f54e72d0188a50961bd9cd7c929bb660dab80bb581f91c60b2ea3", "status": "success", "check": "NOTE", "buildurl": "https://github.com/r-universe/bdwilliamson/actions/runs/38693239223" }, { "r": "4.5.3", "os": "mac", "version": "2.3.8", "date": "2026-10-15T07:01:28.000Z", "commit": "c160a9fe05dbe78c8e31bb9d3f89e8be32509924", "fileid": "https://r2.ropensci.org/125847ad385e12b0c08162aceaa47578f0f4a5de39cdf7e3badb7e712a35efc6", "status": "success", "check": "NOTE", "buildurl": "https://github.com/r-universe/bdwilliamson/actions/runs/38693239223" }, { "r": "4.6.1", "os": "mac", "version": "2.3.8", "date": "2026-10-15T07:02:41.000Z", "commit": "c160a9fe05dbe78c8e31bb9d3f89e8be32509924", "fileid": "https://r2.ropensci.org/f0c94aa21645c094c698d26b2e752e32573db12f634323968607181972397c7a", "status": "success", "check": "NOTE", "buildurl": "https://github.com/r-universe/bdwilliamson/actions/runs/38693239223" }, { "r": "4.6.0", "os": "wasm", "version": "2.3.8", "date": "2026-10-15T06:41:26.000Z", "commit": "c160a9fe05dbe78c8e31bb9d3f89e8be32509924", "fileid": "https://r2.ropensci.org/931d2c2f02380bb2f9e63a89769b89ad15018ade2818ddb4499bae8e7befb85b", "status": "success", "buildurl": "https://github.com/r-universe/bdwilliamson/actions/runs/38693239223" }, { "r": "4.7.0", "os": "win", "version": "2.3.8", "date": "2026-10-15T06:40:32.000Z", "commit": "c160a9fe05dbe78c8e31bb9d3f89e8be32509924", "fileid": "https://r2.ropensci.org/7c3b30b93c32fad9134e4a990806a84fc02ae9e19d5cb3f453aae320a6f51bf5", "status": "success", "check": "NOTE", "buildurl": "https://github.com/r-universe/bdwilliamson/actions/runs/38693239223" }, { "r": "4.5.3", "os": "win", "version": "2.3.8", "date": "2026-10-15T06:40:51.000Z", "commit": "c160a9fe05dbe78c8e31bb9d3f89e8be32509924", "fileid": "https://r2.ropensci.org/006528161cf60f0ba637967ffef31d5b6ea8b33ff13bb31e4b8332b5e1a14f20", "status": "success", "check": "NOTE", "buildurl": "https://github.com/r-universe/bdwilliamson/actions/runs/38693239223" }, { "r": "4.6.1", "os": "win", "version": "2.3.8", "date": "2026-10-15T06:40:31.000Z", "commit": "c160a9fe05dbe78c8e31bb9d3f89e8be32509924", "fileid": "https://r2.ropensci.org/47057677f7455e02883a31dff3f6b5f44f2cd9a30319fee50861df03d218a3ac", "status": "success", "check": "NOTE", "buildurl": "https://github.com/r-universe/bdwilliamson/actions/runs/38693239223" } ] }