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)MERO_0.1.2.zip(r-4.6)MERO_0.1.2.zip(r-4.5)
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✨
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.

1.15 score 14 scripts 249 downloads 4 exports 92 dependencies

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

TargetResultTimeFilesSyslog
linux-devel-x86_64OK164
source / vignettesOK187
linux-release-x86_64OK159
macos-release-arm64OK99
macos-oldrel-arm64OK113
windows-develOK110
windows-releaseOK143
windows-oldrelOK100
wasm-releaseOK119

Exports:EvalImpMEROPlotCorrelateMeanRMSE

Dependencies:abindbackportsbootbroomcarcarDataclicodetoolscolorspacecorrplotcowplotcpp11crayonDerivdigestdoBydoParalleldoRNGdplyrfarverforeachforecastFormulafracdiffgenericsggplot2ggpubrggrepelggsciggsignifgluegridExtragtablehmsisobanditeratorsitertoolslabelinglatticelifecyclelme4lmtestmagrittrMASSMatrixMatrixModelsmgcvmicrobenchmarkminqamissForestmodelrnlmenloptrnnetnumDerivpbkrtestpillarpkgconfigpolynomprettyunitsprogresspurrrquantregR6randomForestrangerrbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreformulasrlangrngtoolsrstatixS7scalesSparseMstringistringrsurvivaltibbletidyrtidyselecttimeDateurcautf8vctrsviridisLitewithrzoo