Package: BoolNet 2.1.9

BoolNet: Construction, Simulation and Analysis of Boolean Networks

Functions to reconstruct, generate, and simulate synchronous, asynchronous, probabilistic, and temporal Boolean networks. Provides also functions to analyze and visualize attractors in Boolean networks <doi:10.1093/bioinformatics/btq124>.

Authors:Christoph Müssel [aut], Martin Hopfensitz [aut], Dao Zhou [aut], Hans A. Kestler [aut, cre], Armin Biere [ctb], Troy D. Hanson [ctb]

BoolNet_2.1.9.tar.gz
BoolNet_2.1.9.zip(r-4.7)BoolNet_2.1.9.zip(r-4.6)BoolNet_2.1.9.zip(r-4.5)
BoolNet_2.1.9.tgz(r-4.6-x86_64)BoolNet_2.1.9.tgz(r-4.6-arm64)BoolNet_2.1.9.tgz(r-4.5-x86_64)BoolNet_2.1.9.tgz(r-4.5-arm64)
BoolNet_2.1.9.tar.gz(r-4.7-arm64)BoolNet_2.1.9.tar.gz(r-4.7-x86_64)BoolNet_2.1.9.tar.gz(r-4.6-arm64)BoolNet_2.1.9.tar.gz(r-4.6-x86_64)
BoolNet_2.1.9.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
BoolNet/json (API)
NEWS

# Install 'BoolNet' in R:
install.packages('BoolNet', repos = c('https://hkestler.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:
  • cellcycle - Mammalian cell cycle network
  • examplePBN - An artificial probabilistic Boolean network
  • igf - Boolean model of the IGF pathway
  • yeastTimeSeries - Yeast cell cycle time series data

On CRAN:

Conda:

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

5.87 score 3 stars 5 packages 166 scripts 696 downloads 73 mentions 41 exports 12 dependencies

Last updated from:af3a714c5b. Checks:11 NOTE, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64NOTE124
linux-devel-x86_64NOTE134
source / vignettesOK175
linux-release-arm64NOTE129
linux-release-x86_64NOTE122
macos-release-arm64NOTE77
macos-release-x86_64NOTE213
macos-oldrel-arm64NOTE102
macos-oldrel-x86_64NOTE159
windows-develNOTE101
windows-releaseNOTE107
windows-oldrelNOTE113
wasm-releaseOK102

Exports:attractorsToLaTeXbinarizeTimeSerieschooseNetworkfixGenesgenerateCanalyzinggenerateNestedCanalyzinggenerateRandomNKNetworkgenerateStategenerateTimeSeriesgetAttractorsgetAttractorSequencegetBasinOfAttractiongetPathToAttractorgetStateSummarygetTransitionProbabilitiesgetTransitionTableloadBioTapestryloadNetworkloadSBMLmarkovSimulationperturbNetworkperturbTrajectoriesplotAttractorsplotNetworkWiringplotPBNTransitionsplotSequenceplotStateGraphreconstructNetworksaveNetworksequenceToLaTeXsimplifyNetworksimulateSymbolicModelstateTransitionsymbolicToTruthTabletestAttractorRobustnesstestIndegreetestNetworkPropertiestestTransitionRobustnesstoPajektoSBMLtruthTableToSymbolic

Dependencies:clicpp11glueigraphlatticelifecyclemagrittrMatrixpkgconfigrlangvctrsXML

Detailed introduction to all major features of BoolNet

Rendered fromBoolNet_package_vignette.Snwusingutils::Sweaveon May 23 2026.

Last update: 2022-09-04
Started: 2013-11-20

Readme and manuals

Help Manual

Help pageTopics
Create LaTeX state table of attractorsattractorsToLaTeX
Binarize a set of real-valued time seriesbinarizeTimeSeries
Mammalian cell cycle networkcellcycle
Extract a single Boolean network from a probabilistic Boolean networkchooseNetwork
An artificial probabilistic Boolean networkexamplePBN
Simulate knocked-out or over-expressed genesfixGenes
Generate a random N-K Boolean networkgenerateRandomNKNetwork
Generate a state vector from single gene valuesgenerateState
Generate time series from a networkgenerateTimeSeries
Generation functions for biologically relevant function classesgenerateCanalyzing generateNestedCanalyzing
Identify attractors in a Boolean networkgetAttractors
Decode the state sequence of a synchronous attractorgetAttractorSequence
Get states in basin of attractiongetBasinOfAttraction
Get state transitions between a state and its attractorgetPathToAttractor
Retrieve summary information on a stategetStateSummary
Get a matrix of transitions and their probabilities in probabilistic Boolean networksgetTransitionProbabilities
Retrieve the transition table of a networkgetTransitionTable
Boolean model of the IGF pathwayigf
Import a network from BioTapestryloadBioTapestry
Load a Boolean network from a fileloadNetwork
Load an SBML documentloadSBML
Identify important states in probabilistic Boolean networksmarkovSimulation
Perturb a Boolean network randomlyperturbNetwork
Perturb the state trajectories and calculate robustness measuresperturbTrajectories
Plot state tables or transition graphs of attractorsplotAttractors
Plot the wiring of a Boolean networkplotNetworkWiring
Visualize the transitions in a probabilistic Boolean networkplotPBNTransitions
Plot a sequence of statesplotSequence
Visualize state transitions and attractor basinsplotStateGraph
Print attractor cyclesprint.AttractorInfo
Print a Boolean networkprint.BooleanNetwork
Print the results of a Markov chain simulationprint.MarkovSimulation
Print a probabilistic Boolean networkprint.BooleanNetworkCollection print.ProbabilisticBooleanNetwork
Print simulation resultsprint.SymbolicSimulation
Print a transition tableprint.BooleanStateInfo print.TransitionTable
Reconstruct a Boolean network from time series of measurementsreconstructNetwork
Save a networksaveNetwork
Create LaTeX table of state sequencessequenceToLaTeX sequenceToLaTeX.BooleanNetwork sequenceToLaTeX.data.frame
Simplify the functions of a synchronous, asynchronous, or probabilistic Boolean networksimplifyNetwork
Simulate a symbolic Boolean networksimulateSymbolicModel
Perform a transition to the next statestateTransition
Convert a symbolic network into a truth table representationsymbolicToTruthTable
Test properties of networks by comparing them to random networkstestAttractorRobustness testIndegree testNetworkProperties testTransitionRobustness
Export a network to the Pajek file formattoPajek
Export a network to SBMLtoSBML
Convert a network in truth table representation into a symbolic representationtruthTableToSymbolic
Yeast cell cycle time series datayeastTimeSeries