Package: MERO 0.1.2

MERO: Performing Monte Carlo Expectation Maximization Random Forest Imputation for Biological Data

Perform missing value imputation for biological data using the random forest algorithm, the imputation aim to keep the original mean and standard deviation consistent after imputation.

Authors:Mohamed Soudy [aut, cre]

MERO_0.1.2.tar.gz
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MERO_0.1.2.tgz(r-4.4-any)MERO_0.1.2.tgz(r-4.3-any)
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MERO.pdf |MERO.html✨
MERO/json (API)

# Install 'MERO' in R:
install.packages('MERO', repos = c('https://mohmedsoudy.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.08 score 12 scripts 231 downloads 4 exports 82 dependencies

Last updated 2 years agofrom:4988b8ecf7. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 20 2024
R-4.5-winOKNov 20 2024
R-4.5-linuxOKNov 20 2024
R-4.4-winOKNov 20 2024
R-4.4-macOKNov 20 2024
R-4.3-winOKNov 20 2024
R-4.3-macOKNov 20 2024

Exports:EvalImpMEROPlotCorrelateMeanRMSE

Dependencies:abindbackportsbootbroomcarcarDataclicodetoolscolorspacecorrplotcowplotcpp11crayonDerivdigestdoBydoParalleldoRNGdplyrfansifarverforeachFormulagenericsggplot2ggpubrggrepelggsciggsignifgluegridExtragtablehmsisobanditeratorsitertoolslabelinglatticelifecyclelme4magrittrMASSMatrixMatrixModelsmgcvmicrobenchmarkminqamissForestmodelrmunsellnlmenloptrnnetnumDerivpbkrtestpillarpkgconfigpolynomprettyunitsprogresspurrrquantregR6randomForestRColorBrewerRcppRcppEigenrlangrngtoolsrstatixscalesSparseMstringistringrsurvivaltibbletidyrtidyselectutf8vctrsviridisLitewithr