Package: GRENITS 1.57.0

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

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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-1.57.0(bioc 3.20)GRENITS-1.56.0(bioc 3.19)

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

bioconductor-package

11 exports 1.00 score 34 dependencies 8 mentions

Last updated 2 months agofrom:436f5ebc9a

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 colorspace fansi farver ggplot2 glue gtable isoband labeling lattice lifecycle magrittr MASS Matrix mgcv munsell nlme pillar pkgconfig plyr R6 RColorBrewer Rcpp RcppArmadillo reshape2 rlang scales stringi stringr tibble utf8 vctrs viridisLite withr

GRENITS

Rendered fromGRENITS_package.Rnwusingutils::Sweaveon Jun 11 2024.

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