Package: semmcmc 0.0.6

semmcmc: Bayesian Structural Equation Modeling in Multiple Omics Data Integration

Provides Markov Chain Monte Carlo (MCMC) routine for the structural equation modelling described in Maity et. al. (2020) <doi:10.1093/bioinformatics/btaa286>. This MCMC sampler is useful when one attempts to perform an integrative survival analysis for multiple platforms of the Omics data where the response is time to event and the predictors are different omics expressions for different platforms.

Authors:Arnab Maity [aut, cre], Sang Chan Lee [aut], Bani K. Mallick [aut], Samsiddhi Bhattacharjee [aut], Nidhan K. Biswas [aut]

semmcmc_0.0.6.tar.gz
semmcmc_0.0.6.zip(r-4.7)semmcmc_0.0.6.zip(r-4.6)semmcmc_0.0.6.zip(r-4.5)
semmcmc_0.0.6.tgz(r-4.6-any)semmcmc_0.0.6.tgz(r-4.5-any)
semmcmc_0.0.6.tar.gz(r-4.7-any)semmcmc_0.0.6.tar.gz(r-4.6-any)
semmcmc_0.0.6.tgz(r-4.6-emscripten)
manual.pdf |manual.html
DESCRIPTION
card.svg |card.png
semmcmc/json (API)

# Install 'semmcmc' in R:
install.packages('semmcmc', repos = c('https://maitya02.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/maitya02/semmcmc/issues

On CRAN:

Conda:

2.70 score 36 downloads 1 exports 18 dependencies

Last updated from:c174b470cd. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK113
source / vignettesOK161
linux-release-x86_64OK99
macos-release-arm64OK211
macos-oldrel-arm64OK148
windows-develOK79
windows-releaseOK62
windows-oldrelOK63
wasm-releaseOK95

Exports:mcmc

Dependencies:cliexpmgenericsgluelatticelifecyclemagrittrMASSMatrixmsmmvtnormpillarpkgconfigrlangsurvivaltibbleutf8vctrs