Package: miceRanger 1.5.1
Sam Wilson
miceRanger: Multiple Imputation by Chained Equations with Random Forests
Multiple Imputation has been shown to be a flexible method to impute missing values by Van Buuren (2007) <doi:10.1177/0962280206074463>. Expanding on this, random forests have been shown to be an accurate model by Stekhoven and Buhlmann <arxiv:1105.0828> to impute missing values in datasets. They have the added benefits of returning out of bag error and variable importance estimates, as well as being simple to run in parallel.
Authors:
miceRanger_1.5.1.tar.gz
miceRanger_1.5.1.zip(r-4.5)miceRanger_1.5.1.zip(r-4.4)miceRanger_1.5.1.zip(r-4.3)
miceRanger_1.5.1.tgz(r-4.5-any)miceRanger_1.5.1.tgz(r-4.4-any)miceRanger_1.5.1.tgz(r-4.3-any)
miceRanger_1.5.1.tar.gz(r-4.5-noble)miceRanger_1.5.1.tar.gz(r-4.4-noble)
miceRanger_1.5.1.tgz(r-4.4-emscripten)miceRanger_1.5.1.tgz(r-4.3-emscripten)
miceRanger.pdf |miceRanger.html✨
miceRanger/json (API)
NEWS
| # Install 'miceRanger' in R: |
| install.packages('miceRanger', repos = c('https://farrellday.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/farrellday/miceranger/issues
- sampleMiceDefs - Sample miceDefs object built off of iris dataset. Included so examples don't run for too long.
imputation-methodsmachine-learningmicemissing-datamissing-valuesrandom-forests
Last updated 3 years agofrom:4b87a65189. Checks:1 OK, 8 NOTE. Indexed: yes.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Mar 10 2025 |
| R-4.5-win | NOTE | Mar 10 2025 |
| R-4.5-mac | NOTE | Mar 10 2025 |
| R-4.5-linux | NOTE | Mar 10 2025 |
| R-4.4-win | NOTE | Mar 10 2025 |
| R-4.4-mac | NOTE | Mar 10 2025 |
| R-4.4-linux | NOTE | Mar 10 2025 |
| R-4.3-win | NOTE | Mar 10 2025 |
| R-4.3-mac | NOTE | Mar 10 2025 |
Exports:addDatasetsaddIterationsamputeDatacompleteDatagetVarImpsimputemiceRangerplotCorrelationsplotDistributionsplotImputationVarianceplotModelErrorplotVarConvergenceplotVarImportance
Dependencies:abindaskpassbackportsbitbit64bootbroomcarcarDatacellrangerclassclicliprcodetoolscolorspacecorrplotcowplotcpp11crayoncurldata.tableDerivDescToolsdoBydplyre1071ExactexpmfansifarverFNNforcatsforeachFormulagenericsggplot2ggpubrggrepelggsciggsignifgldgluegridExtragtablehavenhmshttrisobanditeratorsjsonlitelabelinglatticelifecyclelme4lmommagrittrMASSMatrixMatrixModelsmgcvmicrobenchmarkmimeminqamodelrmunsellmvtnormnlmenloptrnnetnumDerivopensslpbkrtestpillarpkgconfigpolynomprettyunitsprogressproxypurrrquantregR6rangerrbibutilsRColorBrewerRcppRcppEigenRdpackreadrreadxlreformulasrematchrlangrootSolverstatixrstudioapiscalesSparseMstringistringrsurvivalsystibbletidyrtidyselecttzdbutf8vctrsviridisLitevroomwithr
Diagnostic Plotting
Rendered fromdiagnosticPlotting.Rmdusingknitr::rmarkdownon Mar 10 2025.Last update: 2020-02-17
Started: 2020-01-23
Imputing Missing Data with miceRanger
Rendered fromusingMiceRanger.Rmdusingknitr::rmarkdownon Mar 10 2025.Last update: 2020-02-17
Started: 2020-01-09
The MICE Algorithm
Rendered frommiceAlgorithm.Rmdusingknitr::rmarkdownon Mar 10 2025.Last update: 2020-02-17
Started: 2020-01-23
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| addDatasets | addDatasets |
| addIterations | addIterations |
| amputeData | amputeData |
| completeData | completeData |
| Get Variable Imputations | getVarImps |
| Impute New Data With Existing Models | impute |
| miceRanger: Fast Imputation with Random Forests | miceRanger |
| plotCorrelations | plotCorrelations |
| plotDistributions | plotDistributions |
| plotImputationVariance | plotImputationVariance |
| plotModelError | plotModelError |
| plotVarConvergence | plotVarConvergence |
| plotVarImportance | plotVarImportance |
| Print a 'miceDefs' object | print.miceDefs |
| Sample miceDefs object built off of iris dataset. Included so examples don't run for too long. | sampleMiceDefs |
