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  "Date": "2024-02-19",
  "Title": "Ternary Network Estimation",
  "Description": "Gene-regulatory network (GRN) modeling seeks to infer\ndependencies between genes and thereby provide insight into the\nregulatory relationships that exist within a cell. This package\nprovides a computational Bayesian approach to GRN estimation\nfrom perturbation experiments using a ternary network model, in\nwhich gene expression is discretized into one of 3 states: up,\nunchanged, or down). The ternarynet package includes a parallel\nimplementation of the replica exchange Monte Carlo algorithm\nfor fitting network models, using MPI.",
  "Author": "Matthew N. McCall <mccallm@gmail.com>, Anthony Almudevar\n<Anthony_Alumudevar@urmc.rochester.edu>, David Burton\n<David_Burton@urmc.rochester.edu>, Harry Stern\n<harry.stern@rochester.edu>",
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  "License": "GPL (>= 2)",
  "biocViews": "Software, CellBiology, GraphAndNetwork, Network, Bayesian",
  "git_url": "https://git.bioconductor.org/packages/ternarynet",
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  "Maintainer": "McCall N. Matthew <mccallm@gmail.com>",
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  "Repository": "https://bioc.r-universe.dev",
  "Date/Publication": "2026-04-28 12:36:17 UTC",
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  "_published": "2026-07-04T18:16:13.629Z",
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    "ternaryPost",
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      "page": "graphPosterior",
      "title": "Network Topology",
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    },
    {
      "page": "parallelFit",
      "title": "Fit ternary network models using parallel tempering",
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      "page": "plotFit",
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    },
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      "page": "plotPost",
      "title": "Network Posterior Plot",
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        "plotPost"
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    },
    {
      "page": "plotTraces",
      "title": "Network Fit Traces",
      "topics": [
        "plotTraces"
      ]
    },
    {
      "page": "predictAttractor",
      "title": "Predict the attractor(s) resulting from a given perturbation",
      "topics": [
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      ]
    },
    {
      "page": "simulateSteadyState",
      "title": "Simulate Steady State Data",
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