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:Sam Wilson [aut, cre]

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-4.4-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'))

Peer review:

Bug tracker:https://github.com/farrellday/miceranger/issues

Datasets:
  • sampleMiceDefs - Sample miceDefs object built off of iris dataset. Included so examples don't run for too long.

On CRAN:

imputation-methodsmachine-learningmicemissing-datamissing-valuesrandom-forests

7.08 score 67 stars 1 packages 40 scripts 472 downloads 13 exports 107 dependencies

Last updated 3 years agofrom:4b87a65189. Checks:OK: 1 NOTE: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 10 2024
R-4.5-winNOTENov 10 2024
R-4.5-linuxNOTENov 10 2024
R-4.4-winNOTENov 10 2024
R-4.4-macNOTENov 10 2024
R-4.3-winNOTENov 10 2024
R-4.3-macNOTENov 10 2024

Exports:addDatasetsaddIterationsamputeDatacompleteDatagetVarImpsimputemiceRangerplotCorrelationsplotDistributionsplotImputationVarianceplotModelErrorplotVarConvergenceplotVarImportance

Dependencies:abindaskpassbackportsbitbit64bootbroomcarcarDatacellrangerclassclicliprcodetoolscolorspacecorrplotcowplotcpp11crayoncurldata.tableDerivDescToolsdoBydplyre1071ExactexpmfansifarverFNNforcatsforeachFormulagenericsggplot2ggpubrggrepelggsciggsignifgldgluegridExtragtablehavenhmshttrisobanditeratorsjsonlitelabelinglatticelifecyclelme4lmommagrittrMASSMatrixMatrixModelsmgcvmicrobenchmarkmimeminqamodelrmunsellmvtnormnlmenloptrnnetnumDerivopensslpbkrtestpillarpkgconfigpolynomprettyunitsprogressproxypurrrquantregR6rangerRColorBrewerRcppRcppEigenreadrreadxlrematchrlangrootSolverstatixrstudioapiscalesSparseMstringistringrsurvivalsystibbletidyrtidyselecttzdbutf8vctrsviridisLitevroomwithr

Diagnostic Plotting

Rendered fromdiagnosticPlotting.Rmdusingknitr::rmarkdownon Nov 10 2024.

Last update: 2020-02-17
Started: 2020-01-23

Imputing Missing Data with miceRanger

Rendered fromusingMiceRanger.Rmdusingknitr::rmarkdownon Nov 10 2024.

Last update: 2020-02-17
Started: 2020-01-09

The MICE Algorithm

Rendered frommiceAlgorithm.Rmdusingknitr::rmarkdownon Nov 10 2024.

Last update: 2020-02-17
Started: 2020-01-23