Methods for High-Dimensional Multi-View Learning
Overview
Methods for high-dimensional multi-view learning based on the multi-view stacking (MVS) framework. For technical details on the MVS and stacked penalized logistic regression (StaPLR) methods see Van Loon, Fokkema, Szabo, & De Rooij (2020) doi:10.1016/j.inffus.2020.03.007 and Van Loon et al. (2022) doi:10.3389/fnins.2022.830630.
Install
Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK13 OK2026-06-09
- ERROR12 OK, 1 ERROR2026-06-08
- OK13 OK2026-05-12
Show 2 earlier changesHide 2 earlier changes
- WARNING12 OK, 1 WARNING2026-05-11
- OK14 OK2026-03-10
Documentation
- Help pages
- 11includes a package overview page
- Examples that run
- 0%10 only when donttest examples are enabled (R CMD check --as-cran)
- Exported functions with examples
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 0%
Downloads
Dependencies
Code & Tests
People & History
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- RR 4.6.0 released · 2026-04-24
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- RR 4.5.0 released · 2025-04-11
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- RR 4.4.0 released · 2024-04-24
- 1.0.22023-08-15
- RR 4.3.0 released · 2023-04-21
Package metadata
- First published
- 2023-08-15
- Total releases
- 3 / 3 yrs
- License
- GPL-2 OSI
- Download size
- 3.7 MB
Cite
Cite this package
Run in R for the authors' entry:
citation("mvs")The package provides a citation file, so citation() returns the authors' own entry.
It points to 10.1016/j.inffus.2020.03.007, 10.3389/fnins.2022.830630, 10.1016/j.inffus.2024.102524.
Cite the R Observatory
For a number measured here: a download total, a coverage figure, an archival date.
From data release v2026-10-25, which the citation names so these numbers can be found later. More on citing and the projects behind them.