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  "Title": "Stochastic Modelling for Systems Biology",
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  "Author": "Darren Wilkinson",
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  "Description": "Code and data for modelling and simulation of stochastic\nkinetic biochemical network models. It contains the code and\ndata associated with the second and third editions of the book\nStochastic Modelling for Systems Biology, published by Chapman\n& Hall/CRC Press.",
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        "Name",
        "Shoe.size",
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      "tojson": true
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      "title": "Stochastic Modelling for Systems Biology",
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        "smfsb",
        "SMfSB2e",
        "smfsb2e",
        "SMfSB3e",
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      "title": "Discretise output from a discrete event simulation algorithm",
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      "title": "Simulate a sample path from a stochastic kinetic model described by a stochastic Petri net",
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    {
      "page": "gillespied",
      "title": "Simulate a sample path from a stochastic kinetic model described by a stochastic Petri net",
      "topics": [
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    },
    {
      "page": "imdeath",
      "title": "Simulate a sample path from the homogeneous immigration-death process",
      "topics": [
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    {
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      "title": "Example simulated time courses from a stochastic Lotka-Volterra model",
      "topics": [
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        "LVirregular",
        "LVirregularNoise10",
        "LVnoise10",
        "LVnoise10Scale10",
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        "LVnoise3010",
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        "LVpreyNoise10",
        "LVpreyNoise10Scale10"
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    {
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      "title": "Summarise and plot tabular MCMC output",
      "topics": [
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    {
      "page": "metrop",
      "title": "Run a simple Metropolis sampler with standard normal target and uniform innovations",
      "topics": [
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      "title": "Run a Metropolis-Hastings MCMC algorithm for the parameters of a Bayesian posterior distribution",
      "topics": [
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    {
      "page": "mytable",
      "title": "Simple example data frame",
      "topics": [
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    {
      "page": "normgibbs",
      "title": "A simple Gibbs sampler for Bayesian inference for the mean and precision of a normal random sample",
      "topics": [
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    },
    {
      "page": "pfMLLik",
      "title": "Create a function for computing the log of an unbiased estimate of marginal likelihood of a time course data set",
      "topics": [
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    },
    {
      "page": "pfMLLik1",
      "title": "Create a function for computing the log of an unbiased estimate of marginal likelihood of a time course data set",
      "topics": [
        "pfMLLik1"
      ]
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    {
      "page": "rcfmc",
      "title": "Simulate a continuous time finite state space Markov chain",
      "topics": [
        "rcfmc"
      ]
    },
    {
      "page": "rdiff",
      "title": "Simulate a sample path from a univariate diffusion process",
      "topics": [
        "rdiff"
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    },
    {
      "page": "rfmc",
      "title": "Simulate a finite state space Markov chain",
      "topics": [
        "rfmc"
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    {
      "page": "simpleEuler",
      "title": "Simulate a sample path from an ODE model",
      "topics": [
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      ]
    },
    {
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      "title": "Simulate a many realisations of a model at a given fixed time in the future given an initial time and state, using a function (closure) for advancing the state of the model",
      "topics": [
        "simSample"
      ]
    },
    {
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      "title": "Simulate a model at a specified set of times, using a function (closure) for advancing the state of the model",
      "topics": [
        "simTimes"
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    },
    {
      "page": "simTs",
      "title": "Simulate a model on a regular grid of times, using a function (closure) for advancing the state of the model",
      "topics": [
        "simTs"
      ]
    },
    {
      "page": "simTs1D",
      "title": "Simulate a model on a regular grid of times, using a function (closure) for advancing the state of the model",
      "topics": [
        "simTs1D"
      ]
    },
    {
      "page": "simTs2D",
      "title": "Simulate a model on a regular grid of times, using a function (closure) for advancing the state of the model",
      "topics": [
        "simTs2D"
      ]
    },
    {
      "page": "spnModels",
      "title": "Example SPN models",
      "topics": [
        "BD",
        "Dimer",
        "ID",
        "LV",
        "LVV",
        "MM",
        "SEIR",
        "SIR",
        "spnModels"
      ]
    },
    {
      "page": "StepCLE",
      "title": "Create a function for advancing the state of an SPN by using a simple Euler-Maruyama integration method for the approximating CLE",
      "topics": [
        "StepCLE"
      ]
    },
    {
      "page": "StepCLE1D",
      "title": "Create a function for advancing the state of an SPN by using a simple Euler-Maruyama discretisation of the CLE on a 1D regular grid",
      "topics": [
        "StepCLE1D"
      ]
    },
    {
      "page": "StepCLE2D",
      "title": "Create a function for advancing the state of an SPN by using a simple Euler-Maruyama discretisation of the CLE on a 2D regular grid",
      "topics": [
        "StepCLE2D"
      ]
    },
    {
      "page": "StepEuler",
      "title": "Create a function for advancing the state of an ODE model by using a simple Euler integration method",
      "topics": [
        "StepEuler"
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    },
    {
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      "title": "Create a function for advancing the state of an SPN by using a simple continuous deterministic Euler integration method",
      "topics": [
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      ]
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    {
      "page": "StepFRM",
      "title": "Create a function for advancing the state of an SPN by using Gillespie's first reaction method",
      "topics": [
        "StepFRM"
      ]
    },
    {
      "page": "StepGillespie",
      "title": "Create a function for advancing the state of an SPN by using the Gillespie algorithm",
      "topics": [
        "StepGillespie"
      ]
    },
    {
      "page": "StepGillespie1D",
      "title": "Create a function for advancing the state of an SPN by using the Gillespie algorithm on a 1D regular grid",
      "topics": [
        "StepGillespie1D"
      ]
    },
    {
      "page": "StepGillespie2D",
      "title": "Create a function for advancing the state of an SPN by using the Gillespie algorithm on a 2D regular grid",
      "topics": [
        "StepGillespie2D"
      ]
    },
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      "page": "stepLVc",
      "title": "A function for advancing the state of a Lotka-Volterra model by using the Gillespie algorithm",
      "topics": [
        "stepLVc"
      ]
    },
    {
      "page": "StepODE",
      "title": "Create a function for advancing the state of an ODE model by using the deSolve package",
      "topics": [
        "StepODE"
      ]
    },
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      "page": "StepPTS",
      "title": "Create a function for advancing the state of an SPN by using a simple approximate Poisson time stepping method",
      "topics": [
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      ]
    },
    {
      "page": "StepSDE",
      "title": "Create a function for advancing the state of an SDE model by using a simple Euler-Maruyama integration method",
      "topics": [
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