| 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 |