Package: iClusterVB 0.1.4

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_0.1.4.tar.gz
iClusterVB_0.1.4.zip(r-4.7)iClusterVB_0.1.4.zip(r-4.6)iClusterVB_0.1.4.zip(r-4.5)
iClusterVB_0.1.4.tgz(r-4.6-x86_64)iClusterVB_0.1.4.tgz(r-4.6-arm64)iClusterVB_0.1.4.tgz(r-4.5-x86_64)iClusterVB_0.1.4.tgz(r-4.5-arm64)
iClusterVB_0.1.4.tar.gz(r-4.7-arm64)iClusterVB_0.1.4.tar.gz(r-4.7-x86_64)iClusterVB_0.1.4.tar.gz(r-4.6-arm64)iClusterVB_0.1.4.tar.gz(r-4.6-x86_64)
iClusterVB_0.1.4.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
iClusterVB/json (API)

# Install 'iClusterVB' in R:

install.packages('iClusterVB', repos = c('https://abdalkarima.r-universe.dev', 'https://cloud.r-project.org'))

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:

openblas cpp openmp

3.70 score 1 stars 7 scripts 162 downloads 3 exports 71 dependencies

Last updated from:6f0593bc5a. Checks:13 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	OK	205
linux-devel-x86_64	OK	223
source / vignettes	OK	307
linux-release-arm64	OK	207
linux-release-x86_64	OK	220
macos-release-arm64	OK	175
macos-release-x86_64	OK	338
macos-oldrel-arm64	OK	235
macos-oldrel-x86_64	OK	538
windows-devel	OK	195
windows-release	OK	185
windows-oldrel	OK	166
wasm-release	OK	149

Exports:chmap iClusterVB piplot

Dependencies:base64enc bslib cachem cli cluster clustMixType coda combinat commonmark cowplot cpp11 digest dplyr farver fastmap fontawesome fs generics ggplot2 glue gtable htmltools httpuv isoband jquerylib jsonlite labeling later lattice lifecycle magrittr MASS Matrix MatrixModels mclust mcmc MCMCpack memoise mgcv mime mvtnorm nlme otel pheatmap pillar pkgconfig poLCA promises quantreg R6 rappdirs RColorBrewer Rcpp RcppArmadillo rlang S7 sass scales scatterplot3d shiny sourcetools SparseM survival tibble tidyselect utf8 VarSelLCM vctrs viridisLite withr xtable

Introduction to iClusterVB

Rendered fromintro_to_iClusterVB.Rmdusingknitr::rmarkdown

Last update: 2024-12-07
Started: 2024-07-29

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Generates a heat map based on an iClusterVB object	chmap
Fast Integrative Clustering for High-Dimensional Multi-View Data Using Variational Bayesian Inference	iClusterVB
LAML (Acute Myeloid Leukemia) Data	laml laml.cli laml.exp laml.mut
Generates a probability inclusion plot based on an iClusterVB object	piplot
Generic plot method for `iClusterVB` objects	plot.iClusterVB
Simulated Dataset	sim_data
Generic summary method for `iClusterVB` objects	summary.iClusterVB