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✨
DESCRIPTION
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:

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

2.94 score 87 scripts 237 downloads 35 exports 1 dependencies

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

Target	Result	Time
linux-devel-arm64	OK	167
linux-devel-x86_64	OK	121
source / vignettes	OK	128
linux-release-arm64	OK	112
linux-release-x86_64	OK	163
macos-release-arm64	OK	84
macos-release-x86_64	OK	158
macos-oldrel-arm64	OK	102
macos-oldrel-x86_64	OK	191
windows-devel	OK	89
windows-release	OK	89
windows-oldrel	OK	79
wasm-release	OK	95

Exports:abcRun abcSmc as.timedData discretise gillespie gillespied imdeath mcmcSummary metrop metropolisHastings normgibbs pfMLLik pfMLLik1 rcfmc rdiff rfmc simpleEuler simSample simTimes simTs simTs1D simTs2D StepCLE StepCLE1D StepCLE2D StepEuler StepEulerSPN StepFRM StepGillespie StepGillespie1D StepGillespie2D stepLVc StepODE StepPTS StepSDE

Dependencies:abind

smfsb

Rendered fromsmfsb.Snwusingutils::Sweave

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

Citation

Readme and manuals

Help Manual

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

Package: smfsb 1.5

smfsb: Stochastic Modelling for Systems Biology

Citation

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)