Package: smfsb 1.5

smfsb: Stochastic Modelling for Systems Biology

Code and data for modelling and simulation of stochastic kinetic biochemical network models. It contains the code and data associated with the second and third editions of the book Stochastic Modelling for Systems Biology, published by Chapman & Hall/CRC Press.

Authors:Darren Wilkinson

smfsb_1.5.tar.gz
smfsb_1.5.zip(r-4.7)smfsb_1.5.zip(r-4.6)smfsb_1.5.zip(r-4.5)
smfsb_1.5.tgz(r-4.6-x86_64)smfsb_1.5.tgz(r-4.6-arm64)smfsb_1.5.tgz(r-4.5-x86_64)smfsb_1.5.tgz(r-4.5-arm64)
smfsb_1.5.tar.gz(r-4.7-arm64)smfsb_1.5.tar.gz(r-4.7-x86_64)smfsb_1.5.tar.gz(r-4.6-arm64)smfsb_1.5.tar.gz(r-4.6-x86_64)
smfsb_1.5.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
smfsb/json (API)

# Install 'smfsb' in R:
install.packages('smfsb', repos = c('https://darrenjw.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:
  • BD - Example SPN models
  • Dimer - Example SPN models
  • ID - Example SPN models
  • LV - Example SPN models
  • 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
  • MM - Example SPN models
  • mytable - Simple example data frame
  • SEIR - Example SPN models
  • SIR - Example SPN models

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.91 score 81 scripts 185 downloads 35 exports 1 dependencies

Last updated from:16df57d89e. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK124
linux-devel-x86_64OK117
source / vignettesOK164
linux-release-arm64OK142
linux-release-x86_64OK115
macos-release-arm64OK178
macos-release-x86_64OK227
macos-oldrel-arm64OK244
macos-oldrel-x86_64OK436
windows-develOK102
windows-releaseOK125
windows-oldrelOK85
wasm-releaseOK130

Exports:abcRunabcSmcas.timedDatadiscretisegillespiegillespiedimdeathmcmcSummarymetropmetropolisHastingsnormgibbspfMLLikpfMLLik1rcfmcrdiffrfmcsimpleEulersimSamplesimTimessimTssimTs1DsimTs2DStepCLEStepCLE1DStepCLE2DStepEulerStepEulerSPNStepFRMStepGillespieStepGillespie1DStepGillespie2DstepLVcStepODEStepPTSStepSDE

Dependencies:abind

smfsb

Rendered fromsmfsb.Snwusingutils::Sweaveon May 14 2026.

Last update: 2018-08-30
Started: 2011-11-01

Readme and manuals

Help Manual

Help pageTopics
Stochastic Modelling for Systems BiologySMfSB smfsb SMfSB2e smfsb2e SMfSB3e smfsb3e
Run a set of simulations initialised with parameters sampled from a given prior distribution, and compute statistics required for an ABC analaysisabcRun
Run an ABC-SMC algorithm for infering the parameters of a forward modelabcSmc
Convert a time series object to a timed data matrixas.timedData
Discretise output from a discrete event simulation algorithmdiscretise
Simulate a sample path from a stochastic kinetic model described by a stochastic Petri netgillespie
Simulate a sample path from a stochastic kinetic model described by a stochastic Petri netgillespied
Simulate a sample path from the homogeneous immigration-death processimdeath
Example simulated time courses from a stochastic Lotka-Volterra modelLVdata LVirregular LVirregularNoise10 LVnoise10 LVnoise10Scale10 LVnoise30 LVnoise3010 LVperfect LVprey LVpreyNoise10 LVpreyNoise10Scale10
Summarise and plot tabular MCMC outputmcmcSummary
Run a simple Metropolis sampler with standard normal target and uniform innovationsmetrop
Run a Metropolis-Hastings MCMC algorithm for the parameters of a Bayesian posterior distributionmetropolisHastings
Simple example data framemytable
A simple Gibbs sampler for Bayesian inference for the mean and precision of a normal random samplenormgibbs
Create a function for computing the log of an unbiased estimate of marginal likelihood of a time course data setpfMLLik
Create a function for computing the log of an unbiased estimate of marginal likelihood of a time course data setpfMLLik1
Simulate a continuous time finite state space Markov chainrcfmc
Simulate a sample path from a univariate diffusion processrdiff
Simulate a finite state space Markov chainrfmc
Simulate a sample path from an ODE modelsimpleEuler
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 modelsimSample
Simulate a model at a specified set of times, using a function (closure) for advancing the state of the modelsimTimes
Simulate a model on a regular grid of times, using a function (closure) for advancing the state of the modelsimTs
Simulate a model on a regular grid of times, using a function (closure) for advancing the state of the modelsimTs1D
Simulate a model on a regular grid of times, using a function (closure) for advancing the state of the modelsimTs2D
Example SPN modelsBD Dimer ID LV LVV MM SEIR SIR spnModels
Create a function for advancing the state of an SPN by using a simple Euler-Maruyama integration method for the approximating CLEStepCLE
Create a function for advancing the state of an SPN by using a simple Euler-Maruyama discretisation of the CLE on a 1D regular gridStepCLE1D
Create a function for advancing the state of an SPN by using a simple Euler-Maruyama discretisation of the CLE on a 2D regular gridStepCLE2D
Create a function for advancing the state of an ODE model by using a simple Euler integration methodStepEuler
Create a function for advancing the state of an SPN by using a simple continuous deterministic Euler integration methodStepEulerSPN
Create a function for advancing the state of an SPN by using Gillespie's first reaction methodStepFRM
Create a function for advancing the state of an SPN by using the Gillespie algorithmStepGillespie
Create a function for advancing the state of an SPN by using the Gillespie algorithm on a 1D regular gridStepGillespie1D
Create a function for advancing the state of an SPN by using the Gillespie algorithm on a 2D regular gridStepGillespie2D
A function for advancing the state of a Lotka-Volterra model by using the Gillespie algorithmstepLVc
Create a function for advancing the state of an ODE model by using the deSolve packageStepODE
Create a function for advancing the state of an SPN by using a simple approximate Poisson time stepping methodStepPTS
Create a function for advancing the state of an SDE model by using a simple Euler-Maruyama integration methodStepSDE