R: Stochastic Modelling for Systems Biology

# Stochastic Modelling for Systems Biology

## Documentation for package ‘smfsb’ version 1.5

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Code demos. Use demo() to run them.

## Help Pages

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