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
MERO_0.1.2.zip(r-4.5)MERO_0.1.2.zip(r-4.4)MERO_0.1.2.zip(r-4.3)
MERO_0.1.2.tgz(r-4.5-any)MERO_0.1.2.tgz(r-4.4-any)MERO_0.1.2.tgz(r-4.3-any)
MERO_0.1.2.tar.gz(r-4.5-noble)MERO_0.1.2.tar.gz(r-4.4-noble)
MERO_0.1.2.tgz(r-4.4-emscripten)MERO_0.1.2.tgz(r-4.3-emscripten)
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'))

On CRAN:

Conda:

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

1.23 score 17 scripts 193 downloads 4 exports 85 dependencies

Last updated 2 years agofrom:4988b8ecf7. Checks:9 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 20 2025
R-4.5-winOKMar 20 2025
R-4.5-macOKMar 20 2025
R-4.5-linuxOKMar 20 2025
R-4.4-winOKMar 20 2025
R-4.4-macOKMar 20 2025
R-4.4-linuxOKMar 20 2025
R-4.3-winOKMar 20 2025
R-4.3-macOKMar 20 2025

Exports:EvalImpMEROPlotCorrelateMeanRMSE

Dependencies:abindbackportsbootbroomcarcarDataclicodetoolscolorspacecorrplotcowplotcpp11crayonDerivdigestdoBydoParalleldoRNGdplyrfansifarverforeachFormulagenericsggplot2ggpubrggrepelggsciggsignifgluegridExtragtablehmsisobanditeratorsitertoolslabelinglatticelifecyclelme4magrittrMASSMatrixMatrixModelsmgcvmicrobenchmarkminqamissForestmodelrmunsellnlmenloptrnnetnumDerivpbkrtestpillarpkgconfigpolynomprettyunitsprogresspurrrquantregR6randomForestrbibutilsRColorBrewerRcppRcppEigenRdpackreformulasrlangrngtoolsrstatixscalesSparseMstringistringrsurvivaltibbletidyrtidyselectutf8vctrsviridisLitewithr