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-4.7-any)MERO_0.1.2.zip(r-4.6-any)MERO_0.1.2.zip(r-4.5-any)
MERO_0.1.2.tgz(r-4.6-any)MERO_0.1.2.tgz(r-4.5-any)
MERO_0.1.2.tar.gz(r-4.7-any)MERO_0.1.2.tar.gz(r-4.6-any)
MERO_0.1.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
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.

1.08 score 12 scripts 272 downloads 4 exports 91 dependencies

Last updated from:4988b8ecf7. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK134
source / vignettesOK226
linux-releaseOK144
macos-releaseOK136
macos-oldrelOK100
windows-develOK107
windows-releaseOK106
windows-oldrelOK108
wasm-releaseOK114

Exports:EvalImpMEROPlotCorrelateMeanRMSE

Dependencies:abindbackportsbootbroomcarcarDataclicodetoolscolorspacecorrplotcowplotcpp11crayonDerivdigestdoBydoParalleldoRNGdplyrfarverforeachforecastFormulafracdiffgenericsggplot2ggpubrggrepelggsciggsignifgluegridExtragtablehmsisobanditeratorsitertoolslabelinglatticelifecyclelme4lmtestmagrittrMASSMatrixMatrixModelsmgcvminqamissForestmodelrnlmenloptrnnetnumDerivpbkrtestpillarpkgconfigpolynomprettyunitsprogresspurrrquantregR6randomForestrangerrbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreformulasrlangrngtoolsrstatixS7scalesSparseMstringistringrsurvivaltibbletidyrtidyselecttimeDateurcautf8vctrsviridisLitewithrzoo