{ "_id": "6a12d098acfb0bcc41d220b5", "Package": "mlim", "Type": "Package", "Title": "Single and Multiple Imputation with Automated Machine Learning", "Version": "0.4.0", "Authors@R": "person(\"E. F. Haghish\",\nrole = c(\"aut\", \"cre\", \"cph\"),\nemail = \"haghish@hotmail.com\")", "Description": "Machine learning algorithms have been used for performing\nsingle missing data imputation and most recently, multiple\nimputations. However, this is the first attempt for using\nautomated machine learning algorithms for performing both\nsingle and multiple imputation. Automated machine learning is a\nprocedure for fine-tuning the model automatic, performing a\nrandom search for a model that results in less error, without\noverfitting the data. The main idea is to allow the model to\nset its own parameters for imputing each variable separately\ninstead of setting fixed predefined parameters to impute all\nvariables of the dataset. Using automated machine learning, the\npackage fine-tunes an Elastic Net (default) or Gradient\nBoosting, Random Forest, Deep Learning, Extreme Gradient\nBoosting, or Stacked Ensemble machine learning model (from one\nor a combination of other supported algorithms) for imputing\nthe missing observations. This procedure has been implemented\nfor the first time by this package and is expected to\noutperform other packages for imputing missing data that do not\nfine-tune their models. The multiple imputation is implemented\nvia bootstrapping without letting the duplicated observations\nto harm the cross-validation procedure, which is the way\nimputed variables are evaluated. Most notably, the package\nimplements automated procedure for handling imputing imbalanced\ndata (class rarity problem), which happens when a factor\nvariable has a level that is far more prevalent than the\nother(s). This is known to result in biased predictions, hence,\nbiased imputation of missing data. However, the autobalancing\nprocedure ensures that instead of focusing on maximizing\naccuracy (classification error) in imputing factor variables, a\nfairer procedure and imputation method is practiced.", "License": "MIT + file LICENSE", "Encoding": "UTF-8", "RoxygenNote": "7.3.1", "LazyData": "true", "URL": "https://github.com/haghish/mlim,\nhttps://www.sv.uio.no/psi/english/people/academic/haghish/", "BugReports": "https://github.com/haghish/mlim/issues", "Config/pak/sysreqs": "cmake make default-jdk libicu-dev libssl-dev\nlibx11-dev zlib1g-dev", "Repository": "https://haghish.r-universe.dev", "Date/Publication": "2026-02-22 18:35:41 UTC", "RemoteUrl": "https://github.com/haghish/mlim", "RemoteRef": "HEAD", "RemoteSha": "1c7ea69ab9e96ec36feb95b04205dbd3490b6496", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-24 10:15:11 UTC", "User": "root" }, "Author": "E. F. Haghish [aut, cre, cph]", "Maintainer": "E. F. Haghish ", "MD5sum": "9572b69c5391dd541bbd1e7554bf123c", "_user": "haghish", "_type": "src", "_file": "mlim_0.4.0.tar.gz", "_fileid": "e280a48746e73973616ea8bc492b9ee26a6ba4f0afba9c3702e9fed289ac03f1", "_filesize": 1048688, "_sha256": "e280a48746e73973616ea8bc492b9ee26a6ba4f0afba9c3702e9fed289ac03f1", "_created": "2026-05-24T10:15:11.000Z", "_published": "2026-05-24T10:19:04.083Z", "_distro": "noble", "_jobs": [ { "job": 77589625835, "time": 193, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "7184324844" }, { "job": 77589625840, "time": 180, "config": "linux-release-x86_64", "r": "4.6.0", "check": "OK", "artifact": "7184323655" }, { "job": 77589625827, "time": 105, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "OK", "artifact": "7184316391" }, { "job": 77589625839, "time": 89, "config": "macos-release-arm64", "r": "4.6.0", "check": "OK", "artifact": "7184314983" }, { "job": 77589409596, "time": 211, "config": "source", "r": "4.6.0", "check": "OK", "artifact": "7184306260" }, { "job": 77589625833, "time": 129, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "7184318683" }, { "job": 77589625834, "time": 120, "config": "windows-devel", "r": "4.7.0", "check": "OK", "artifact": "7184317899" }, { "job": 77589625831, "time": 108, "config": "windows-oldrel", "r": "4.5.3", "check": "OK", "artifact": "7184316646" }, { "job": 77589625837, "time": 112, "config": "windows-release", "r": "4.6.0", "check": "OK", "artifact": "7184317188" } ], "_buildurl": "https://github.com/r-universe/haghish/actions/runs/26358444063", "_status": "success", "_host": "GitHub-Actions", "_upstream": "https://github.com/haghish/mlim", "_commit": { "id": "1c7ea69ab9e96ec36feb95b04205dbd3490b6496", "author": "E. 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