Function reference • uqsa

User Functions

exported functions

ABCMCMC(): Performs and Approximate Bayesian Computation Sampling of Model Parameters

checkFitWithPreviousExperiments(): ABC acceptance of currently sampled values given old data (Prior)

checkModel(): checkModel tries to establish the simulation file for a given model

dCopulaPrior(): copulaPrior creates a prior probability density function

dNormalPrior(): dNormalPrior creates the density function of a multivariate normal distribution with independent components

dUniformPrior(): dUniformPrior creates a uniform density function

defaultAcceptance(): default ABC acceptance probability function for one experiment

defaultDistance(): default distance function for one experiment

fitCopula(): Makes a Probability Density Estimate (from a sample)

getMCMCPar(): Selects MCMC scheme specific setup parameters

globalSensitivity(): Global Sensitivity Analysis

importReactionsSSA(): Functions to construct and run the stochastic simulation using GillespieSSA2 package

log10ParMap(): LOG10 parameter mapping used by the MCMC module

log10ParMapJac(): LOG10 parameter mapping, jacobian

makeGillespieModel(): Create a list of reactions for GillespieSSA2

makeIndepCopula(): Copula Formulation for Uniform Prior Distributions

makeObjective(): creates Objective functions from ingredients

makeObjectiveSSA(): Function that creates the objective function

mcmc(): Markov Chain Monte Carlo

mcmcInit(): Initialize the Markov chain

mcmcUpdate(): This function proposes an MCMC candidate variable, and either accepts or rejects the candidate

parUpdate(): Updates Parameter Values

plotSimualtionsFromSBtab(): Simulate and plot Data and Simulation

preCalibration(): Determine a starting value for ABC's delta

rCopulaPrior(): rCopulaPrior returns a function that generates random values from the copula model

rNormalPrior(): rNormalPrior returns a random vector generator

rUniformPrior(): rUniformPrior returns a random vector generator

sensitivity.graph(): plot the sensitivity matrix

simulator.c(): This creates a closure that simulates the model

uqsa_example(): Load an example model for this package

`%otherwise%`: This function can be used to specify default values

smmala_move(): SMMALA move

smmala_move_density(): SMMALA transition kernel density

ggplotTimeSeries(): Plot time series simulations with experimental data

ggplotTimeSeriesStates(): Plot time series simulation with state variables

change_temperature(): Should 2 Markov chains exchange their temperatures

loadSample_mpi(): This function merges mpi-samples into one

mcmc_mpi(): The MPI version of the mcmc function

simc(): This creates a closure that simulates the model, similar to simulator.c

Internal Functions

not exported functions

convert.parameter(): Convert ODE parameter to Gillespie parameter

match.coefficients(): find the coefficients in a formula

match.names(): Find the variable names in a formula

observable.mean.in.bin(): The mean value of an observable value given a parameter bin

parameter.from.kinetic.law(): Attempt to find multiplicative reaction rate coefficients

parse.formula(): Splits a formula into a left and right side

parse.kinetic(): Find forward and backward component in a reaction kinetic

propensity(): propensity creates a propensity formula

sum.of.bin.variance(): Weighted Sum of Bin-specific variances

uqsa uqsa-package: uqsa: Uncertainty Quanitification and Global Sensitivity Analysis via ABC sampling

getDose(): Get the values of the input for a series of dose response experiments

plotDoseResponse(): Plot dose response simulations with experimental data

plotTimeSeries(): Plot time series simulations with experimental data

Rmpi_swap_temperatures(): Communicate with other ranks and swap beta

fisherInformationFromGSA(): Calculate Global Fisher Information

fisherInformationFunc(): Fisher Information from Sensitivity

gatherSample(): gatherSample collects all sample points, from all files, with the given temperature

gradLogLikelihoodFunc(): Default log-likelihood function, gradient

gradLog_NormalPrior(): Gradient of the logarithm of a normal prior

`%has%`: checks whether a variable has the named attributes

loadSubSample_mpi(): This function merges mpi-samples into one

logLikelihoodFunc(): Default log-likelihood function

pbdMPI_bcast_reduce_temperatures(): Broadcast to other ranks and swap temperatures with any of them

pbdMPI_swap_temperatures(): Communicate with other ranks and swap beta

sampleWithNoise(): (for testing) A non-Copula sampling function as fallback

simcf(): This creates a closure that simulates the model, similar to simulator.c

swap_points_locally(): Swap the end-points of two Markov chains