# An introduction to R package `mvs`

arXiv: https://arxiv.org/abs/2504.17546 · HTML: https://mlsanity.com/paper/2504.17546/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 28 references that are in the map.
- First posted on arXiv 24 Apr 2025.

## Builds on (most cited first, 3 of 3)

- [Extended Comparisons of Best Subset Selection, Forward Stepwise Selection, and the Lasso](https://mlsanity.com/paper/1707.08692/) (2017)
- [Stacked Penalized Logistic Regression for Selecting Views in Multi-View Learning](https://mlsanity.com/paper/1811.02316/) (2018)
- [View selection in multi-view stacking: Choosing the meta-learner](https://mlsanity.com/paper/2010.16271/) (2020)

## Abstract (arXiv)

In biomedical science, a set of objects or persons can often be described by multiple distinct sets of features obtained from different data sources or modalities (called "multi-view data"). Classical machine learning methods ignore the multi-view structure of such data, limiting model interpretability and performance. The R package `mvs` provides methods that were designed specifically for dealing with multi-view data, based on the multi-view stacking (MVS) framework. MVS is a form of supervised (machine) learning used to train multi-view classification or prediction models. MVS works by training a learning algorithm on each view separately, estimating the predictive power of each view-specific model through cross-validation, and then using another learning algorithm to assign weights to the view-specific models based on their estimated predictions. MVS is a form of ensemble learning, dividing the large multi-view learning problem into smaller sub-problems. Most of these sub-problems can be solved in parallel, making it computationally attractive. Additionally, the number of features of the sub-problems is greatly reduced compared with the full multi-view learning problem. This makes MVS especially useful when the total number of features is larger than the number of observations (i.e., high-dimensional data). MVS can still be applied even if the sub-problems are themselves high-dimensional by adding suitable penalty terms to the learning algorithms. Furthermore, MVS can be used to automatically select the views which are most important for prediction. The R package `mvs` makes fitting MVS models, including such penalty terms, easily and openly accessible. `mvs` allows for the fitting of stacked models with any number of levels, with different penalty terms, different outcome distributions, and provides several options for missing data handling.