SMfSB 2e: Stochastic Modelling for Systems Biology, second edition


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Documentation for package ‘smfsb’ version 1.1

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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
mcmcSummary Summarise and plot tabular MCMC output
metrop Run a simple Metropolis sampler with standard normal target and uniform innovations
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
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
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
SMfSB Stochastic Modelling for Systems Biology, second edition
smfsb Stochastic Modelling for Systems Biology, second edition
SMfSB2e Stochastic Modelling for Systems Biology, second edition
smfsb2e Stochastic Modelling for Systems Biology, second edition
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
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
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