Stochastic Modelling for Systems Biology

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Documentation for package ‘smfsb’ version 1.5

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abcRun Run a set of simulations initialised with parameters sampled from a given prior distribution, and compute statistics required for an ABC analaysis
abcSmc Run an ABC-SMC algorithm for infering the parameters of a forward model
as.timedData Convert a time series object to a timed data matrix
BD Example SPN models
Dimer Example SPN models
discretise Discretise output from a discrete event simulation algorithm
gillespie Simulate a sample path from a stochastic kinetic model described by a stochastic Petri net
gillespied Simulate a sample path from a stochastic kinetic model described by a stochastic Petri net
ID Example SPN models
imdeath Simulate a sample path from the homogeneous immigration-death process
LV Example SPN models
LVdata Example simulated time courses from a stochastic Lotka-Volterra model
LVirregular Example simulated time courses from a stochastic Lotka-Volterra model
LVirregularNoise10 Example simulated time courses from a stochastic Lotka-Volterra model
LVnoise10 Example simulated time courses from a stochastic Lotka-Volterra model
LVnoise10Scale10 Example simulated time courses from a stochastic Lotka-Volterra model
LVnoise30 Example simulated time courses from a stochastic Lotka-Volterra model
LVnoise3010 Example simulated time courses from a stochastic Lotka-Volterra model
LVperfect Example simulated time courses from a stochastic Lotka-Volterra model
LVprey Example simulated time courses from a stochastic Lotka-Volterra model
LVpreyNoise10 Example simulated time courses from a stochastic Lotka-Volterra model
LVpreyNoise10Scale10 Example simulated time courses from a stochastic Lotka-Volterra model
LVV Example SPN models
mcmcSummary Summarise and plot tabular MCMC output
metrop Run a simple Metropolis sampler with standard normal target and uniform innovations
metropolisHastings Run a Metropolis-Hastings MCMC algorithm for the parameters of a Bayesian posterior distribution
MM Example SPN models
mytable Simple example data frame
normgibbs A simple Gibbs sampler for Bayesian inference for the mean and precision of a normal random sample
pfMLLik Create a function for computing the log of an unbiased estimate of marginal likelihood of a time course data set
pfMLLik1 Create a function for computing the log of an unbiased estimate of marginal likelihood of a time course data set
rcfmc Simulate a continuous time finite state space Markov chain
rdiff Simulate a sample path from a univariate diffusion process
rfmc Simulate a finite state space Markov chain
SEIR Example SPN models
simpleEuler Simulate a sample path from an ODE model
simSample 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
simTimes Simulate a model at a specified set of times, using a function (closure) for advancing the state of the model
simTs Simulate a model on a regular grid of times, using a function (closure) for advancing the state of the model
simTs1D Simulate a model on a regular grid of times, using a function (closure) for advancing the state of the model
simTs2D Simulate a model on a regular grid of times, using a function (closure) for advancing the state of the model
SIR Example SPN models
SMfSB Stochastic Modelling for Systems Biology
smfsb Stochastic Modelling for Systems Biology
SMfSB2e Stochastic Modelling for Systems Biology
smfsb2e Stochastic Modelling for Systems Biology
SMfSB3e Stochastic Modelling for Systems Biology
smfsb3e Stochastic Modelling for Systems Biology
spnModels Example SPN models
StepCLE Create a function for advancing the state of an SPN by using a simple Euler-Maruyama integration method for the approximating CLE
StepCLE1D 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
StepCLE2D 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
StepEuler Create a function for advancing the state of an ODE model by using a simple Euler integration method
StepEulerSPN Create a function for advancing the state of an SPN by using a simple continuous deterministic Euler integration method
StepFRM Create a function for advancing the state of an SPN by using Gillespie's first reaction method
StepGillespie Create a function for advancing the state of an SPN by using the Gillespie algorithm
StepGillespie1D Create a function for advancing the state of an SPN by using the Gillespie algorithm on a 1D regular grid
StepGillespie2D Create a function for advancing the state of an SPN by using the Gillespie algorithm on a 2D regular grid
stepLVc A function for advancing the state of a Lotka-Volterra model by using the Gillespie algorithm
StepODE Create a function for advancing the state of an ODE model by using the deSolve package
StepPTS Create a function for advancing the state of an SPN by using a simple approximate Poisson time stepping method
StepSDE Create a function for advancing the state of an SDE model by using a simple Euler-Maruyama integration method