Package index • uqsa

Model and data

Functions for loading model and data and construct a mathematical model (ODE or stochastic) and corresponding code.

uqsa_example(): Loads an example model for this package
model_from_tsv(): Loads the content from a series of TSV files
as_ode(): Interprets a model as an ODE
as_cme(): Interprets the provided biological reaction model as stochastic
write_and_compile(): Writes code to a file and compiles
values(): Finds the values in a data.frame
uncertainty(): Finds the uncertainty of values in a data.frame
experiments(): Extracts measured data and simulation experiment instructions
standard_error_matrix(): Standard Error Matrix from an errors object
conservation_law_analysis(): Reduces the size of the system
kinetic_law_matrix(): Represents the kinetic laws of the model as a two column matrix
replace_powers(): Interprets caret notation of arithmetic powers for C
stoichiometry(): Returns a list of named stoichiometric vectors
stoichiometric_matrix(): Infers the stoichiometric matrix of a reaction network
unit.from.string(): Interprets a unit string
unit.id(): Converts a unit to a string that works as an identifier
unit_as_character(): Converts a unit data.frame into a printable string
units_from_table(): Finds units column in a data.frame and returns the values
yJacobian(): Calculates the Jacobian of string-math
`c_path<-`(): Adds information about the model's C code
c_path(): Retrieves information about the model's C code
generate_code(): Constructs Code
print(<cme>): Prints a Summary about the CME model
print(<ode>): Prints a summary about the ode
shlib(): Compiles C code to shared library
`so_path<-`(): Adds information about compiled code
so_path(): Retrieves information about compiled code
write_c_code(): Writes C code to a file

Simulations

Functions for simulating the model

simulator.c(): Creates a closure that simulates the model
simstoch(): Simulates a Stochastic Model
simfi(): Creates a closure that simulates the model, similar to simulator.c
scrnn(): Returns a closure around gsl_odeiv2_CRNN()
gsl_odeiv2_fi(): Simulates an ode model
gsl_odeiv2_CRNN(): Simulates a CRNN ode model
name_method(): Reverse look-up of method name from key

Prior

Functions for constructing priors

rNormalPrior(): Returns a random vector generator
rUniformPrior(): Returns a random vector generator
rCopulaPrior(): Returns a function that generates random values from the copula model
dNormalPrior(): Creates the density function of a multivariate normal distribution with independent components
dUniformPrior(): Creates a uniform density function
dCopulaPrior(): Creates a prior probability density function
gNormalPrior(): Creates the gradient function of a multivariate normal distribution with independent components, in log-space
fitCopula(): Makes a Probability Density Estimator from a Sample

Uncertainty quantification - likelihood based

MCMC methods for Bayesian parameter inference

mcmc(): Creates a Markov Chain Monte Carlo sampler
mcmc_init(): Initializes the Markov chain
mcmc_mpi(): Creates the MPI version of an MCMC sampler
metropolis_update(): Performs an MCMC update using the Metropolis rule
high_level_metropolis(): Determines a likely Metropolis setup based on the information in the model
smmala_update(): Performs an SMMALA update
high_level_smmala(): Determines a likely SMMALA setup based on the information in the model
logLikelihoodFunc(): Constructs a custom log-likelihood function
ll(): Default log-likelihood function
gllf(): Default gradient-log-likelihood function
fi(): Default Fisher-information function
tune_step_size(): Finds a good step-size for a given MCMC algorithm
loadSample_mpi(): Merges mpi-samples into one
gatherSample(): Collects all sample points, from all files, with the given temperature
gatherReplicas(): Collects statistical Replicas

Uncertainty quantification - ABC

Approximate Bayesian Computation (ABC) MCMC for parameter inference

makeObjective(): Creates objective functions from ingredients
abc_mcmc(): Performs Approximate Bayesian Computation Sampling of Model Parameters via Markov Chain Monte Carlo
ABCSMC(): Performs Approximate Bayesian Computation as a Particle Filter

Global Sensitivity Analysis

gsa_binning(): Performs global sensitivity analysis using the binning method
gsa_saltelli(): Outputs the global sensitivity scores SI and SIT, calculated by the Sobol-Homma-Saltelli method
saltelli_prior(): Creates prior samples for the Sobol-Homma-Saltelli global sensitivity analysis
KLD(): Kullback Leibler Divergence
exact_normal_kld(): Multivariate Normal Distribution KLD

Parameter mappings

Parameter transformations from sampling space back to model space

logParMap(): NATURAL LOG parameter mapping used by the MCMC module
logParMapJac(): NATURAL LOG parameter mapping, jacobian
log10ParMap(): LOG10 parameter mapping used by the MCMC module
log10ParMapJac(): LOG10 parameter mapping, Jacobian
log2ParMap(): LOG2 parameter mapping used by the MCMC module
log2ParMapJac(): LOG2 parameter mapping, Jacobian

Helper Functions and Operators

`%@%`: Fetches an Attribute, with checks
`%as%`: A binary operator on strings with units in them
`%has%`: Checks whether a variable has the named attributes
`%otherwise%`: Specifies default values, as fallback
determinePrefix(): Determines a prefix from a character vector str of similar contents
`modify<-`(): Modifies a value
`base<-`(): Converts values to linear space
column(): Retrieves j-th column with names
print(<experiments>): Prints the simulation experiments
print(<mcmcVariable>): Prints information about the mcmc variable
print(<simulation>): Prints the simulation results
print(<unit_of_measurement>): Prints an interpretation string of a unit
small(): Reduces the sample to its effective size

Plotting functions

highCor(): Orders index-pairs from high to low correlation
pc_dist(): Plots a sample in parallel coordinates
sensitivity.graph(): Plot the result of global sensitivity analysis
showPosterior(): Makes a pairs plot for a sample using density estimation methods

Internal functions

onlyCoefficients(): Returns a list of reaction coefficients
onlyNames(): Returns only the names in a reaction formula
CRNN(): Creates C code for a chemical reaction neural network
clear_yacas_environment(): Clears Yacas variables
defaultDistance(): Default distance function for one experiment
formulae(): Finds a column that contains some kind of mathematic expression in a data.frame
linear_scale(): Interprets a character vector as names of logarithms
parse_concise(): Reads Concise Error Notation
method(): Finds the method id given a named method
plot(<experiments>): Plot function for experiments
`[`(<experiments>): Subsets experiments with preserved class
`[`(<simulation>): Subset simulations with preserved class