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Record W4410893100 · doi:10.21105/joss.07435

openmpp: An R package for dynamic microsimulation modeling using OpenM++

2025· article· en· W4410893100 on OpenAlexaff
Matthew T. Warkentin, John M. Hutchinson, Yibing Ruan, Darren R. Brenner

Bibliographic record

VenueThe Journal of Open Source Software · 2025
Typearticle
Languageen
FieldDecision Sciences
Topicdemographic modeling and climate adaptation
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsR packageMicrosimulationComputer scienceComputational scienceEngineeringTransport engineering

Abstract

fetched live from OpenAlex

OpenM++ is a powerful open-source and cross-platform software and domain-specific language to develop, use, and deploy dynamic microsimulations or agent-based models.The openmpp R package provides a programmatic interface to OpenM++ to simplify the process of connecting to local or remote instances with secure user authentication, communicating with web services, creating, managing, and running large-scale and complex microsimulations, performing scalable and reproducible computations, and gathering results for reproducible reporting.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Software · Consensus signal: none
Teacher disagreement score0.064
Threshold uncertainty score0.214

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.024
Meta-epidemiology (narrow)0.0040.002
Meta-epidemiology (broad)0.0040.005
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0060.004
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0640.033

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.194
GPT teacher head0.472
Teacher spread0.277 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreSoftware

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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