Models and predicts multiple output features in single random forest considering the 
    linear relation among the output features, see details in Rahman et al (2017)<doi:10.1093/bioinformatics/btw765>.

Provides a set of basic and extensible data structures and functions for multivariate analysis, including dimensionality reduction techniques, projection methods, and preprocessing functions. The aim of this package is to offer a flexible and user-friendly framework for multivariate analysis that can be easily extended for custom requirements and specific data analysis tasks.

Fully parametric Bayesian multiple imputation framework for massive multivariate data of different variable types as seen in Demirtas, H. (2017) <doi:10.1007/978-981-10-3307-0_8>.

It performs variable selection in a multivariate linear model by estimating the covariance matrix 
 of the residuals then use it to remove the dependence that may exist among the responses and eventually performs variable selection by using the Lasso criterion.
 The method is described in the paper Perrot-Dockès et al. (2017) <arXiv:1704.00076>.

A multivariate generalization of the emulator package.

Implement 'multiverse' style analyses (Steegen S., Tuerlinckx F, Gelman A., Vanpaemal, W., 2016)
    <doi:10.1177/1745691616658637>, (Dragicevic P., Jansen Y., Sarma A., Kay M., Chevalier F., 2019) <doi:10.1145/3290605.3300295> 
    to show the robustness of statistical inference. 'Multiverse analysis' is a philosophy of 
    statistical reporting where paper authors report the outcomes of many different statistical 
    analyses in order to show how fragile or robust their findings are. 
    The 'multiverse' package (Sarma A., Kale A., Moon M., Taback N., Chevalier F., Hullman J., Kay M., 2021) <doi:10.31219/osf.io/yfbwm>
    allows users to concisely and flexibly implement 'multiverse-style' 
    analysis, which involve declaring alternate ways of performing an analysis step, in R and R Notebooks.

Cooperative learning combines the usual squared error loss of predictions with an agreement penalty to encourage the predictions from different data views to agree. By varying the weight of the agreement penalty, we get a continuum of solutions that include the well-known early and late fusion approaches. Cooperative learning chooses the degree of agreement (or fusion) in an adaptive manner, using a validation set or cross-validation to estimate test set prediction error. In the setting of cooperative regularized linear regression, the method combines the lasso penalty with the agreement penalty (Ding, D., Li, S., Narasimhan, B., Tibshirani, R. (2021) <doi:10.1073/pnas.2202113119>).