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  "Package": "BoolNet",
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  "Title": "Construction, Simulation and Analysis of Boolean Networks",
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  "Date": "2023-10-02",
  "Authors@R": "c(person(\"Christoph\", \"Müssel\", role = \"aut\"),\nperson(\"Martin\", \"Hopfensitz\", role = \"aut\"),\nperson(\"Dao\", \"Zhou\", role = \"aut\"),\nperson(c(\"Hans\", \"A.\"), \"Kestler\", email = \"hans.kestler@uni-ulm.de\", role = c(\"aut\", \"cre\")),\nperson(\"Armin\", \"Biere\", role = \"ctb\", comment=\"contributed PicoSAT code\"),\nperson(c(\"Troy\", \"D.\"), \"Hanson\", role = \"ctb\", comment=\"contributed uthash macros\"))",
  "Description": "Functions to reconstruct, generate, and simulate\nsynchronous, asynchronous, probabilistic, and temporal Boolean\nnetworks. Provides also functions to analyze and visualize\nattractors in Boolean networks\n<doi:10.1093/bioinformatics/btq124>.",
  "License": "Artistic-2.0",
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  "Author": "Christoph Müssel [aut], Martin Hopfensitz [aut], Dao Zhou\n[aut], Hans A. Kestler [aut, cre], Armin Biere [ctb]\n(contributed PicoSAT code), Troy D. Hanson [ctb] (contributed\nuthash macros)",
  "Maintainer": "Hans A. Kestler <hans.kestler@uni-ulm.de>",
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  "Repository": "https://hkestler.r-universe.dev",
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    "binarizeTimeSeries",
    "chooseNetwork",
    "fixGenes",
    "generateCanalyzing",
    "generateNestedCanalyzing",
    "generateRandomNKNetwork",
    "generateState",
    "generateTimeSeries",
    "getAttractors",
    "getAttractorSequence",
    "getBasinOfAttraction",
    "getPathToAttractor",
    "getStateSummary",
    "getTransitionProbabilities",
    "getTransitionTable",
    "loadBioTapestry",
    "loadNetwork",
    "loadSBML",
    "markovSimulation",
    "perturbNetwork",
    "perturbTrajectories",
    "plotAttractors",
    "plotNetworkWiring",
    "plotPBNTransitions",
    "plotSequence",
    "plotStateGraph",
    "reconstructNetwork",
    "saveNetwork",
    "sequenceToLaTeX",
    "simplifyNetwork",
    "simulateSymbolicModel",
    "stateTransition",
    "symbolicToTruthTable",
    "testAttractorRobustness",
    "testIndegree",
    "testNetworkProperties",
    "testTransitionRobustness",
    "toPajek",
    "toSBML",
    "truthTableToSymbolic"
  ],
  "_datasets": [
    {
      "name": "cellcycle",
      "title": "Mammalian cell cycle network",
      "object": "cellcycle",
      "file": "cellcycle.rda",
      "class": [
        "BooleanNetwork"
      ],
      "fields": [],
      "table": false,
      "tojson": false
    },
    {
      "name": "examplePBN",
      "title": "An artificial probabilistic Boolean network",
      "object": "examplePBN",
      "file": "examplePBN.rda",
      "class": [
        "ProbabilisticBooleanNetwork",
        "BooleanNetworkCollection"
      ],
      "fields": [],
      "table": false,
      "tojson": false
    },
    {
      "name": "igf",
      "title": "Boolean model of the IGF pathway",
      "object": "igf",
      "file": "igf.rda",
      "class": [
        "SymbolicBooleanNetwork"
      ],
      "fields": [],
      "table": false,
      "tojson": false
    },
    {
      "name": "yeastTimeSeries",
      "title": "Yeast cell cycle time series data",
      "object": "yeastTimeSeries",
      "file": "yeastTimeSeries.rda",
      "class": [
        "matrix",
        "array"
      ],
      "fields": [],
      "rows": 4,
      "table": true,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "attractorsToLaTeX",
      "title": "Create LaTeX state table of attractors",
      "topics": [
        "attractorsToLaTeX"
      ]
    },
    {
      "page": "binarizeTimeSeries",
      "title": "Binarize a set of real-valued time series",
      "topics": [
        "binarizeTimeSeries"
      ]
    },
    {
      "page": "cellcycle",
      "title": "Mammalian cell cycle network",
      "topics": [
        "cellcycle"
      ]
    },
    {
      "page": "chooseNetwork",
      "title": "Extract a single Boolean network from a probabilistic Boolean network",
      "topics": [
        "chooseNetwork"
      ]
    },
    {
      "page": "examplePBN",
      "title": "An artificial probabilistic Boolean network",
      "topics": [
        "examplePBN"
      ]
    },
    {
      "page": "fixGenes",
      "title": "Simulate knocked-out or over-expressed genes",
      "topics": [
        "fixGenes"
      ]
    },
    {
      "page": "generateRandomNKNetwork",
      "title": "Generate a random N-K Boolean network",
      "topics": [
        "generateRandomNKNetwork"
      ]
    },
    {
      "page": "generateState",
      "title": "Generate a state vector from single gene values",
      "topics": [
        "generateState"
      ]
    },
    {
      "page": "generateTimeSeries",
      "title": "Generate time series from a network",
      "topics": [
        "generateTimeSeries"
      ]
    },
    {
      "page": "generationFunctions",
      "title": "Generation functions for biologically relevant function classes",
      "topics": [
        "generateCanalyzing",
        "generateNestedCanalyzing"
      ]
    },
    {
      "page": "getAttractors",
      "title": "Identify attractors in a Boolean network",
      "topics": [
        "getAttractors"
      ]
    },
    {
      "page": "getAttractorSequence",
      "title": "Decode the state sequence of a synchronous attractor",
      "topics": [
        "getAttractorSequence"
      ]
    },
    {
      "page": "getBasinOfAttraction",
      "title": "Get states in basin of attraction",
      "topics": [
        "getBasinOfAttraction"
      ]
    },
    {
      "page": "getPathToAttractor",
      "title": "Get state transitions between a state and its attractor",
      "topics": [
        "getPathToAttractor"
      ]
    },
    {
      "page": "getStateSummary",
      "title": "Retrieve summary information on a state",
      "topics": [
        "getStateSummary"
      ]
    },
    {
      "page": "getTransitionProbabilities",
      "title": "Get a matrix of transitions and their probabilities in probabilistic Boolean networks",
      "topics": [
        "getTransitionProbabilities"
      ]
    },
    {
      "page": "getTransitionTable",
      "title": "Retrieve the transition table of a network",
      "topics": [
        "getTransitionTable"
      ]
    },
    {
      "page": "igf",
      "title": "Boolean model of the IGF pathway",
      "topics": [
        "igf"
      ]
    },
    {
      "page": "loadBioTapestry",
      "title": "Import a network from BioTapestry",
      "topics": [
        "loadBioTapestry"
      ]
    },
    {
      "page": "loadNetwork",
      "title": "Load a Boolean network from a file",
      "topics": [
        "loadNetwork"
      ]
    },
    {
      "page": "loadSBML",
      "title": "Load an SBML document",
      "topics": [
        "loadSBML"
      ]
    },
    {
      "page": "markovSimulation",
      "title": "Identify important states in probabilistic Boolean networks",
      "topics": [
        "markovSimulation"
      ]
    },
    {
      "page": "perturbNetwork",
      "title": "Perturb a Boolean network randomly",
      "topics": [
        "perturbNetwork"
      ]
    },
    {
      "page": "perturbTrajectories",
      "title": "Perturb the state trajectories and calculate robustness measures",
      "topics": [
        "perturbTrajectories"
      ]
    },
    {
      "page": "plotAttractors",
      "title": "Plot state tables or transition graphs of attractors",
      "topics": [
        "plotAttractors"
      ]
    },
    {
      "page": "plotNetworkWiring",
      "title": "Plot the wiring of a Boolean network",
      "topics": [
        "plotNetworkWiring"
      ]
    },
    {
      "page": "plotPBNTransitions",
      "title": "Visualize the transitions in a probabilistic Boolean network",
      "topics": [
        "plotPBNTransitions"
      ]
    },
    {
      "page": "plotSequence",
      "title": "Plot a sequence of states",
      "topics": [
        "plotSequence"
      ]
    },
    {
      "page": "plotStateGraph",
      "title": "Visualize state transitions and attractor basins",
      "topics": [
        "plotStateGraph"
      ]
    },
    {
      "page": "print.AttractorInfo",
      "title": "Print attractor cycles",
      "topics": [
        "print.AttractorInfo"
      ]
    },
    {
      "page": "print.BooleanNetwork",
      "title": "Print a Boolean network",
      "topics": [
        "print.BooleanNetwork"
      ]
    },
    {
      "page": "print.MarkovSimulation",
      "title": "Print the results of a Markov chain simulation",
      "topics": [
        "print.MarkovSimulation"
      ]
    },
    {
      "page": "print.ProbabilisticBooleanNetwork",
      "title": "Print a probabilistic Boolean network",
      "topics": [
        "print.BooleanNetworkCollection",
        "print.ProbabilisticBooleanNetwork"
      ]
    },
    {
      "page": "print.SymbolicSimulation",
      "title": "Print simulation results",
      "topics": [
        "print.SymbolicSimulation"
      ]
    },
    {
      "page": "print.TransitionTable",
      "title": "Print a transition table",
      "topics": [
        "print.BooleanStateInfo",
        "print.TransitionTable"
      ]
    },
    {
      "page": "reconstructNetwork",
      "title": "Reconstruct a Boolean network from time series of measurements",
      "topics": [
        "reconstructNetwork"
      ]
    },
    {
      "page": "saveNetwork",
      "title": "Save a network",
      "topics": [
        "saveNetwork"
      ]
    },
    {
      "page": "sequenceToLaTeX",
      "title": "Create LaTeX table of state sequences",
      "topics": [
        "sequenceToLaTeX",
        "sequenceToLaTeX.BooleanNetwork",
        "sequenceToLaTeX.data.frame"
      ]
    },
    {
      "page": "simplifyNetwork",
      "title": "Simplify the functions of a synchronous, asynchronous, or probabilistic Boolean network",
      "topics": [
        "simplifyNetwork"
      ]
    },
    {
      "page": "simulateSymbolicModel",
      "title": "Simulate a symbolic Boolean network",
      "topics": [
        "simulateSymbolicModel"
      ]
    },
    {
      "page": "stateTransition",
      "title": "Perform a transition to the next state",
      "topics": [
        "stateTransition"
      ]
    },
    {
      "page": "symbolicToTruthTable",
      "title": "Convert a symbolic network into a truth table representation",
      "topics": [
        "symbolicToTruthTable"
      ]
    },
    {
      "page": "testNetworkProperties",
      "title": "Test properties of networks by comparing them to random networks",
      "topics": [
        "testAttractorRobustness",
        "testIndegree",
        "testNetworkProperties",
        "testTransitionRobustness"
      ]
    },
    {
      "page": "toPajek",
      "title": "Export a network to the Pajek file format",
      "topics": [
        "toPajek"
      ]
    },
    {
      "page": "toSBML",
      "title": "Export a network to SBML",
      "topics": [
        "toSBML"
      ]
    },
    {
      "page": "truthTableToSymbolic",
      "title": "Convert a network in truth table representation into a symbolic representation",
      "topics": [
        "truthTableToSymbolic"
      ]
    },
    {
      "page": "yeastTimeSeries",
      "title": "Yeast cell cycle time series data",
      "topics": [
        "yeastTimeSeries"
      ]
    }
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    "cpp11",
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    "igraph",
    "lattice",
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      "engine": "utils::Sweave",
      "headings": [
        "Introduction",
        "Assembling networks",
        "Network analysis",
        "Import and export",
        "Appendix"
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      "created": "2013-11-20",
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