Package: ternarynet 1.57.0

McCall N. Matthew

ternarynet: Ternary Network Estimation

Gene-regulatory network (GRN) modeling seeks to infer dependencies between genes and thereby provide insight into the regulatory relationships that exist within a cell. This package provides a computational Bayesian approach to GRN estimation from perturbation experiments using a ternary network model, in which gene expression is discretized into one of 3 states: up, unchanged, or down). The ternarynet package includes a parallel implementation of the replica exchange Monte Carlo algorithm for fitting network models, using MPI.

Authors:Matthew N. McCall <[email protected]>, Anthony Almudevar <[email protected]>, David Burton <[email protected]>, Harry Stern <[email protected]>

ternarynet_1.57.0.tar.gz
ternarynet_1.57.0.zip(r-4.7)ternarynet_1.57.0.zip(r-4.6)ternarynet_1.57.0.zip(r-4.5)
ternarynet_1.57.0.tgz(r-4.6-x86_64)ternarynet_1.57.0.tgz(r-4.6-arm64)ternarynet_1.57.0.tgz(r-4.5-x86_64)ternarynet_1.57.0.tgz(r-4.5-arm64)
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ternarynet_1.57.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
ternarynet/json (API)
NEWS

# Install 'ternarynet' in R:
install.packages('ternarynet', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • c++– GNU Standard C++ Library v3

On BioConductor:ternarynet-1.57.0(bioc 3.24)ternarynet-1.56.0(bioc 3.23)

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

softwarecellbiologygraphandnetworknetworkbayesiancpp

3.30 score 3 scripts 334 downloads 85 exports 19 dependencies

Last updated from:c84a1185e7. Checks:1 WARNING, 13 OK. Indexed: yes.

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source / vignettesOK162
linux-release-arm64OK161
linux-release-x86_64OK167
macos-release-arm64OK144
macos-release-x86_64OK241
macos-oldrel-arm64OK115
macos-oldrel-x86_64OK309
windows-develOK444
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wasm-releaseOK128

Exports:attractorSummarybackupStagebackupStage<-beta0beta0<-chi0chi0<-degreeObjMindegreeObjMin<-degreeObjsdegreeObjs<-deltadelta<-edgePenaltyedgePenalty<-epsilonepsilon<-experimentNamesexperimentNames<-finalTemperaturefinalTemperature<-geneNamesgeneNames<-graphObjMingraphObjMin<-graphObjsgraphObjs<-graphPosteriorinputParamsinputParams<-m0m0<-maxDegreemaxDegree<-maxStagemaxStage<-maxTransitionmaxTransition<-minScoreminScore<-nene<-neighborDegreeneighborDegree<-newScorenewScore<-pAddParentpAddParent<-parallelFitperturbationObjperturbationObj<-perturbationTypeperturbationType<-pExchangeParentpExchangeParent<-plotFitplotPostplotTracespNeighborhoodpNeighborhood<-predictAttractorrhorho<-scoresscores<-scoreTypescoreType<-simulateSteadyStatestageCountstageCount<-steadyStateObjsteadyStateObj<-tableObjMintableObjMin<-tableObjstableObjs<-ternaryFitternaryFitParametersternaryPosttnetfittnetposttracestraces<-xSeedxSeed<-

Dependencies:BHBiocParallelclicodetoolscpp11formatRfutile.loggerfutile.optionsglueigraphlambda.rlatticelifecyclemagrittrMatrixpkgconfigrlangsnowvctrs

ternarynet: A Computational Bayesian Approach to Ternary Network Estimation

Rendered fromternarynet.Rnwusingutils::Sweaveon May 30 2026.

Last update: 2021-04-15
Started: 2013-11-01

Readme and manuals

Help Manual

Help pageTopics
Summarize AttractorsattractorSummary
Network TopologygraphPosterior
Fit ternary network models using parallel temperingparallelFit
Network Fit PlotplotFit
Network Posterior PlotplotPost
Network Fit TracesplotTraces
Predict the attractor(s) resulting from a given perturbationpredictAttractor
Simulate Steady State DatasimulateSteadyState
Ternary Network Fitclass:ternaryFit degreeObjMin degreeObjMin,ternaryFit-method degreeObjMin<- degreeObjMin<-,ternaryFit-method dim,ternaryFit-method experimentNames experimentNames,ternaryFit-method experimentNames<- experimentNames<-,ternaryFit-method finalTemperature finalTemperature,ternaryFit-method finalTemperature<- finalTemperature<-,ternaryFit-method geneNames geneNames,ternaryFit-method geneNames<- geneNames<-,ternaryFit-method graphObjMin graphObjMin,ternaryFit-method graphObjMin<- graphObjMin<-,ternaryFit-method initialize,ternaryFit-method inputParams inputParams,ternaryFit-method inputParams<- inputParams<-,ternaryFit-method minScore minScore,ternaryFit-method minScore<- minScore<-,ternaryFit-method newScore newScore,ternaryFit-method newScore<- newScore<-,ternaryFit-method perturbationObj perturbationObj,ternaryFit-method perturbationObj<- perturbationObj<-,ternaryFit-method show,ternaryFit-method stageCount stageCount,ternaryFit-method stageCount<- stageCount<-,ternaryFit-method steadyStateObj steadyStateObj,ternaryFit-method steadyStateObj<- steadyStateObj<-,ternaryFit-method tableObjMin tableObjMin,ternaryFit-method tableObjMin<- tableObjMin<-,ternaryFit-method ternaryFit ternaryFit-class ternaryFit-methods traces traces,ternaryFit-method traces<- traces<-,ternaryFit-method xSeed xSeed,ternaryFit-method xSeed<- xSeed<-,ternaryFit-method
Ternary Network Fitting ParametersbackupStage backupStage,ternaryFitParameters-method backupStage<- backupStage<-,ternaryFitParameters-method beta0 beta0,ternaryFitParameters-method beta0<- beta0<-,ternaryFitParameters-method chi0 chi0,ternaryFitParameters-method chi0<- chi0<-,ternaryFitParameters-method class:ternaryFitParameters delta delta,ternaryFitParameters-method delta<- delta<-,ternaryFitParameters-method edgePenalty edgePenalty,ternaryFitParameters-method edgePenalty<- edgePenalty<-,ternaryFitParameters-method epsilon epsilon,ternaryFitParameters-method epsilon<- epsilon<-,ternaryFitParameters-method initialize,ternaryFitParameters-method m0 m0,ternaryFitParameters-method m0<- m0<-,ternaryFitParameters-method maxDegree maxDegree,ternaryFitParameters-method maxDegree<- maxDegree<-,ternaryFitParameters-method maxStage maxStage,ternaryFitParameters-method maxStage<- maxStage<-,ternaryFitParameters-method maxTransition maxTransition,ternaryFitParameters-method maxTransition<- maxTransition<-,ternaryFitParameters-method ne ne,ternaryFitParameters-method ne<- ne<-,ternaryFitParameters-method neighborDegree neighborDegree,ternaryFitParameters-method neighborDegree<- neighborDegree<-,ternaryFitParameters-method pAddParent pAddParent,ternaryFitParameters-method pAddParent<- pAddParent<-,ternaryFitParameters-method perturbationType perturbationType,ternaryFitParameters-method perturbationType<- perturbationType<-,ternaryFitParameters-method pExchangeParent pExchangeParent,ternaryFitParameters-method pExchangeParent<- pExchangeParent<-,ternaryFitParameters-method pNeighborhood pNeighborhood,ternaryFitParameters-method pNeighborhood<- pNeighborhood<-,ternaryFitParameters-method rho rho,ternaryFitParameters-method rho<- rho<-,ternaryFitParameters-method scoreType scoreType,ternaryFitParameters-method scoreType<- scoreType<-,ternaryFitParameters-method show,ternaryFitParameters-method ternaryFitParameters ternaryFitParameters-class ternaryFitParameters-methods
Ternary Network Posteriorclass:ternaryPost degreeObjs degreeObjs,ternaryPost-method degreeObjs<- degreeObjs<-,ternaryPost-method dim,ternaryPost-method experimentNames,ternaryPost-method experimentNames<-,ternaryPost-method geneNames,ternaryPost-method geneNames<-,ternaryPost-method graphObjs graphObjs,ternaryPost-method graphObjs<- graphObjs<-,ternaryPost-method initialize,ternaryPost-method inputParams,ternaryPost-method inputParams<-,ternaryPost-method perturbationObj,ternaryPost-method perturbationObj<-,ternaryPost-method scores scores,ternaryPost-method scores<- scores<-,ternaryPost-method show,ternaryPost-method steadyStateObj,ternaryPost-method steadyStateObj<-,ternaryPost-method tableObjs tableObjs,ternaryPost-method tableObjs<- tableObjs<-,ternaryPost-method ternaryPost ternaryPost-class ternaryPost-methods
Ternary Network Fittingtnetfit
Ternary Network Posterior Samplingtnetpost