viruslearner: Ensemble Learning for HIV-Related Metrics

Advanced statistical modeling techniques for ensemble learning, specifically tailored to the analysis of lymphocyte counts and viral load data in the context of HIV research. Empowering researchers and practitioners, this tool provides a comprehensive solution for tasks such as analysis, prediction and risk calculation related to key viral metrics. The package incorporates cutting-edge ensemble learning principles, inspired by model stacking techniques of Simon P. Couch and Max Kuhn (2022) <doi:10.21105/joss.04471> and adhering to tidy data principles, offering a robust and reproducible framework for HIV research.

Version: 0.0.2
Depends: R (≥ 2.10)
Imports: dials, dplyr, hardhat, parsnip, recipes, rsample, stacks, stats, tidyselect, tune, workflows, workflowsets, yardstick
Suggests: baguette, broom, cowplot, factoextra, FactoMineR, ggpubr, kernlab, kknn, knitr, nnet, NeuralNetTools, ranger, rmarkdown, rules, testthat (≥ 3.0.0), tidyverse, vdiffr, tidyr, viraldomain, vip
Published: 2024-04-03
Author: Juan Pablo Acuña González ORCID iD [aut, cre, cph], María de los Ángeles Salgado Jiménez ORCID iD [aut], Baltazar Joanico Morales ORCID iD [aut], Juan Villagómez Méndez ORCID iD [aut]
Maintainer: Juan Pablo Acuña González <acua6307 at>
License: MIT + file LICENSE
NeedsCompilation: no
Materials: README NEWS
CRAN checks: viruslearner results


Reference manual: viruslearner.pdf
Vignettes: art-adherence


Package source: viruslearner_0.0.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): viruslearner_0.0.2.tgz, r-oldrel (arm64): viruslearner_0.0.2.tgz, r-release (x86_64): viruslearner_0.0.2.tgz
Old sources: viruslearner archive


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