2023a

Summary

This release reorganizes the code to use closures where possible. This means that how exactly a function performs a task is not important to the caller. Only the argument that changes while sampling remains the explicit argument of the closure. The implicit arguments are used when constructing the closure.

Before, we had long argument lists, with some items remaining constant for the whole duration of the function and between calls:

preCalibration(experiments, modelName, parVal, parIdx, npc, copula, U, Z, Score, nCores, environment)

In this release, this became:

Obj <- makeObjective(experiments[expInd],modelName,getScore,parMap)
out <- preCalibration(Obj, npc, rprior)

where Obj is an objective function (closure) that takes one argument: parABC , the ABC sampling variable.

Functionality

All previous functionality is retained. This release also includes new functions to deal with stochastic models:

• importReactionsSSA
• makeObjectiveSSA

These two make it possible to simulate models using the GillespieSSA2 package.

2022b

• Example Models have Compound Identifiers
• Experiments refer to source publication doi
• UQ is an R package
• jupyter notebook demo
• new solver for ODE problems
• number of chains is now optional everywhere

2022a

• Example Models have Compound Identifiers
• Experiments refer to source publication doi
• UQ is an R package
• jupyter notebook demo
• new solver for ODE problems