Package: iClusterVB 0.1.2

Abdalkarim Alnajjar

iClusterVB: Fast Integrative Clustering and Feature Selection for High Dimensional Data

A variational Bayesian approach for fast integrative clustering and feature selection, facilitating the analysis of multi-view, mixed type, high-dimensional datasets with applications in fields like cancer research, genomics, and more.

Authors:Abdalkarim Alnajjar [aut, cre, cph], Zihang Lu [aut]

iClusterVB_0.1.2.tar.gz
iClusterVB_0.1.2.zip(r-4.5)iClusterVB_0.1.2.zip(r-4.4)iClusterVB_0.1.2.zip(r-4.3)
iClusterVB_0.1.2.tgz(r-4.4-x86_64)iClusterVB_0.1.2.tgz(r-4.4-arm64)iClusterVB_0.1.2.tgz(r-4.3-x86_64)iClusterVB_0.1.2.tgz(r-4.3-arm64)
iClusterVB_0.1.2.tar.gz(r-4.5-noble)iClusterVB_0.1.2.tar.gz(r-4.4-noble)
iClusterVB_0.1.2.tgz(r-4.4-emscripten)iClusterVB_0.1.2.tgz(r-4.3-emscripten)
iClusterVB.pdf |iClusterVB.html✨
iClusterVB/json (API)

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

Peer review:

Bug tracker:https://github.com/abdalkarima/iclustervb/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:
  • laml.cli - LAML (Acute Myeloid Leukemia) Data
  • laml.exp - LAML (Acute Myeloid Leukemia) Data
  • laml.mut - LAML (Acute Myeloid Leukemia) Data
  • sim_data - Simulated Dataset

On CRAN:

archivedpackagesr-universe

3.40 score 5 stars 6 scripts 179 downloads 3 exports 75 dependencies

Last updated 14 days agofrom:e3eccd60c3 (on package/iClusterVB). Checks:OK: 9. Indexed: no.

TargetResultDate
Doc / VignettesOKNov 14 2024
R-4.5-win-x86_64OKNov 14 2024
R-4.5-linux-x86_64OKNov 14 2024
R-4.4-win-x86_64OKNov 14 2024
R-4.4-mac-x86_64OKNov 14 2024
R-4.4-mac-aarch64OKNov 14 2024
R-4.3-win-x86_64OKNov 14 2024
R-4.3-mac-x86_64OKNov 14 2024
R-4.3-mac-aarch64OKNov 14 2024

Exports:chmapiClusterVBpiplot

Dependencies:base64encbslibcachemcliclusterclustMixTypecodacolorspacecombinatcommonmarkcowplotcrayondigestdplyrfansifarverfastmapfontawesomefsgenericsggplot2gluegtablehtmltoolshttpuvisobandjquerylibjsonlitelabelinglaterlatticelifecyclemagrittrMASSMatrixMatrixModelsmclustmcmcMCMCpackmemoisemgcvmimemunsellmvtnormnlmepheatmappillarpkgconfigpoLCApromisesquantregR.methodsS3R.ooR.utilsR6rappdirsRColorBrewerRcppRcppArmadillorlangsassscalesscatterplot3dshinysourcetoolsSparseMsurvivaltibbletidyselectutf8VarSelLCMvctrsviridisLitewithrxtable

Introduction to iClusterVB

Rendered fromintro_to_iClusterVB.Rmdusingknitr::rmarkdownon Nov 14 2024.

Last update: 2024-11-14
Started: 2024-11-14