replica

Create, enrich, validate and organise synthetic populations.

The replica package extends the ready4 framework developed by Hamilton et al. (2024) by providing tools for:

• creating synthetic agents from marginal distributions and contingency tables;

• assigning additional attributes using demographic reference data;

• generating synthetic households;

• validating synthetic population quality using ratify(); and

• visualising validation diagnostics using depict().

The package implements and extends synthetic population generation methods described by de Mooij et al. (2024) and is designed to support health-economic microsimulation modelling and other simulation workflows.

Workflow

The workflow supported by replica can be summarised as:

Aggregate Counts
    ↓
make_agents()
    ↓
Synthetic Agents
    ↓
ReplicaAdder
    ↓
enhance()
    ↓
ratify()
    ↓
depict()
    ↓
Enriched Population
    ↓
ReplicaStructure
    +
ReplicaGrouper
    ↓
manufacture()
    ↓
Synthetic Households

Core workflow methods

replica follows a ready4-style workflow centred on a small number of generic methods:

• procure() for retrieving module contents;

• renew() for updating module contents;

• enhance() for assigning attributes;

• ratify() for generating validation diagnostics;

• depict() for visualising validation results; and

• manufacture() for generating household outputs.

Accessing and updating module contents

Module contents can be retrieved using:

procure(ADDER, slot = "population")

and updated using:

renew(ADDER, population = population_dt)

Together, procure() and renew() provide a consistent interface for reading and updating replica modules.

Getting started

replica documentation is organised around the complete synthetic-population workflow.

To install a development version of replica, run the following commands in your R console:

utils::install.packages("devtools")

devtools::install_github("ready4-dev/replica")

1. Creating synthetic agents

Learn how to create individual synthetic agents from aggregate count data.

Key function:

make_agents()

2. Assigning attributes using contingency tables

Learn how to enrich synthetic agents using demographic contingency tables.

Key class:

ReplicaAdder

3. Generating synthetic households

Learn how to transform enriched agents into realistic household structures.

Key classes:

ReplicaStructure
ReplicaGrouper

4. Evaluating synthetic population quality

Learn how to compare synthetic populations with reference data and assess population quality.

Key methods:

ratify()
procure()
depict()

Validation diagnostics can be generated using:

ADDER <- ratify(ADDER)

inspected using:

ADDER <- procure(ADDER, slot = "validation_results")

and visualised using:

ADDER <- depict(ADDER, type = "difference")

Current status

replica is under active initial development.

Library classes, syntax, documentation and workflows are evolving without the use of deprecation conventions.

This library should currently be used only for exploratory purposes.

Use of AI

replica code, tests and documentation (including vignettes) have all been authored by a human-machine partnership.

Microsoft Copilot has been used intensively in the development of this library.

References

Hamilton MP, Gao C, Wiesner G, Filia KM, Menssink JM, Plencnerova P, Baker DG, McGorry PD, Parker A, Karnon J, Cotton SM. and Mihalopoulos C (2024)

A prototype software framework for transferable computational health economic models and its early application in youth mental health.

PharmacoEconomics.

https://link.springer.com/article/10.1007/s40273-024-01378-8

https://ready4-dev.github.io/ready4/

de Mooij J, Sonnenschein T, Pellegrino M, Dastani M, Ettema D, Logan B and Verstegen JA (2024).

GenSynthPop: generating a spatially explicit synthetic population of individuals and households from aggregated data.

Autonomous Agents and Multi-Agent Systems.

https://link.springer.com/article/10.1007/s10458-024-09680-7