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.

A multivariate generalization of the emulator package.

Implement 'multiverse' style analyses (Steegen S., Tuerlinckx F, Gelman A., Vanpaemal, W., 2016)
    <doi:10.1177/1745691616658637> 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>).

Computation of an estimation of the long-memory parameters and
     the long-run covariance matrix using a multivariate model
	 (Lobato (1999) <doi:10.1016/S0304-4076(98)00038-4>; Shimotsu (2007) <doi:10.1016/j.jeconom.2006.01.003>). Two semi-parametric methods are
	 implemented: a Fourier based approach (Shimotsu (2007) <doi:10.1016/j.jeconom.2006.01.003>) and a wavelet based
	 approach (Achard and Gannaz (2016) <doi:10.1111/jtsa.12170>; Achard and Gannaz (2024) <doi:10.1111/jtsa.12719>). Real and complex wavelets are implemented.