MétaCan
Menu
Back to cohort
Record W4407720618 · doi:10.1016/j.softx.2025.102087

cpp11armadillo: An R package to use the Armadillo C++ library

2025· article· en· W4407720618 on OpenAlexaff
Mauricio Vargas, Jonathan Schneider Malamud

Bibliographic record

VenueSoftwareX · 2025
Typearticle
Languageen
FieldComputer Science
TopicData Analysis with R
Canadian institutionsUniversity of TorontoGlobal Affairs Canada
FundersLinux Foundation
KeywordsArmadilloComputer scienceProgramming languageWorld Wide WebGeology

Abstract

fetched live from OpenAlex

This article introduces ‘cpp11armadillo’, an R package that integrates the highly efficient Armadillo C++ linear algebra library with R through the ‘cpp11’ interface. Designed to offer significant performance improvements for computationally intensive tasks, ‘cpp11armadillo’ simplifies the process of integrating C++ code into R. This package is particularly suited for R users requiring efficient matrix operations, especially in cases where vectorization is not possible. Our benchmarks demonstrate substantial speed gains over native R functions and Rcpp-based setups.

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)0.029
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 score0.154
Threshold uncertainty score0.517

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.029
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0050.004
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.1540.141

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 head0.014
GPT teacher head0.258
Teacher spread0.244 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueSoftwareXSame topicData Analysis with RFrench-language works237,207