Package: GRENITS 1.65.0

Edward Morrissey

GRENITS: Gene Regulatory Network Inference Using Time Series

The package offers four network inference statistical models using Dynamic Bayesian Networks and Gibbs Variable Selection: a linear interaction model, two linear interaction models with added experimental noise (Gaussian and Student distributed) for the case where replicates are available and a non-linear interaction model.

Authors:Edward Morrissey

GRENITS_1.65.0.tar.gz
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GRENITS_1.65.0.tgz(r-4.6-x86_64)GRENITS_1.65.0.tgz(r-4.6-arm64)GRENITS_1.65.0.tgz(r-4.5-x86_64)GRENITS_1.65.0.tgz(r-4.5-arm64)
GRENITS_1.65.0.tar.gz(r-4.7-arm64)GRENITS_1.65.0.tar.gz(r-4.7-x86_64)GRENITS_1.65.0.tar.gz(r-4.6-arm64)GRENITS_1.65.0.tar.gz(r-4.6-x86_64)
GRENITS_1.65.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
GRENITS/json (API)
NEWS

# Install 'GRENITS' in R:

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

Uses libs:

openblas– Optimized BLAS
c++– GNU Standard C++ Library v3

Datasets:

Athaliana_ODE - Gene expression time series generated with ODE model
Athaliana_ODE_4NoiseReps - Gene expression time series generated with ODE model with added noise

On BioConductor:GRENITS-1.65.0(bioc 3.24)GRENITS-1.64.0(bioc 3.23)

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

networkinference generegulation timecourse graphandnetwork geneexpression network bayesian openblas cpp

4.20 score 2 scripts 405 downloads 8 mentions 11 exports 24 dependencies

Last updated from:82b57b43aa. Checks:1 ERROR, 11 NOTE, 2 OK. Indexed: yes.

Target	Result	Time
bioc-checks	ERROR	203
linux-devel-arm64	NOTE	279
linux-devel-x86_64	NOTE	297
source / vignettes	OK	328
linux-release-arm64	NOTE	265
linux-release-x86_64	NOTE	334
macos-release-arm64	NOTE	200
macos-release-x86_64	NOTE	389
macos-oldrel-arm64	NOTE	226
macos-oldrel-x86_64	NOTE	325
windows-devel	NOTE	301
windows-release	NOTE	242
windows-oldrel	NOTE	288
wasm-release	OK	136

Exports:analyse.output LinearNet mcmc.defaultParams_gauss mcmc.defaultParams_Linear mcmc.defaultParams_nonLinear mcmc.defaultParams_student NonLinearNet plotPriors read.chain ReplicatesNet_gauss ReplicatesNet_student

Dependencies:cli cpp11 farver ggplot2 glue gtable isoband labeling lifecycle magrittr plyr R6 RColorBrewer Rcpp RcppArmadillo reshape2 rlang S7 scales stringi stringr vctrs viridisLite withr

GRENITS

Rendered fromGRENITS_package.Rnwusingutils::Sweaveon May 30 2026.

Last update: 2013-11-01
Started: 2013-11-01

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Analysis Plots	analyse.output
Gene expression time series generated with ODE model	Athaliana_ODE
Gene expression time series generated with ODE model with added noise	Athaliana_ODE_4NoiseReps
Dynamic Bayesian Network Inference Using Linear Interactions	LinearNet
Default Parameters for Linear Model with Gaussian distributed replicates	mcmc.defaultParams_gauss
Default Parameters for Linear Model	mcmc.defaultParams_Linear
Default Parameters for non-Linear Model	mcmc.defaultParams_nonLinear
Default Parameters for Linear Model with Student distributed replicates	mcmc.defaultParams_student
Dynamic Bayesian Network Inference Using Non-Linear Interactions	NonLinearNet
Plot prior using parameter vector	plotPriors
Read MCMC Chains	read.chain
Dynamic Bayesian Network Inference Using Linear Interactions and Gaussian Experimental Noise	ReplicatesNet_gauss
Dynamic Bayesian Network Inference Using Linear Interactions and Student Experimental Noise	ReplicatesNet_student