Fits the Multiple Random Dot Product Graph Model and performs a test
	 for whether two networks come from the same distribution. Both methods are 
	 proposed in Nielsen, A.M., Witten, D., (2018) "The Multiple Random Dot Product Graph
	 Model", arXiv preprint <arXiv:1811.12172> (Submitted to Journal of Computational
	 and Graphical Statistics).

We introduce a high-dimensional multi-study robust factor model, which learns latent features and accounts for the heterogeneity among source. It could be used for analyzing heterogeneous RNA sequencing data. More details can be referred to Jiang et al. (2025) <doi:10.48550/arXiv.2506.18478>.

Functions to calculate Unique Trait Combinations (UTC) and scaled
    Unique Trait Combinations (sUTC) as measures of multivariate richness. The
    package can also calculate beta-diversity for trait richness and can
    partition this into nestedness-related and turnover components. The code
    will also calculate several measures of overlap. See Keyel and Wiegand
    (2016) <doi:10.1111/2041-210X.12558> for more details.

Multi-penalty linear, logistic and cox ridge regression, including estimation of the penalty parameters by efficient (repeated) cross-validation and marginal likelihood maximization. Multiple high-dimensional data types that require penalization are allowed, as well as unpenalized variables. Paired and preferential data types can be specified. See Van de Wiel et al. (2021), <arXiv:2005.09301>.

A flexible general-purpose toolbox for implementing Rescorla-Wagner models 
    in multi-armed bandit tasks. 
  As the successor and functional extension of the 'binaryRL' package, 
    'multiRL' modularizes the Markov Decision Process (MDP) into six core 
    components. This framework enables users to construct custom models via 
    intuitive if-else syntax and define latent learning rules for agents. 
  For parameter estimation, it provides both likelihood-based 
    inference (MLE and MAP) and simulation-based inference (ABC and 
    RNN), with full support for parallel processing across subjects. 
  The workflow is highly standardized, featuring four main functions 
    that strictly follow the four-step protocol (and ten rules) 
    proposed by Wilson & Collins (2019) <doi:10.7554/eLife.49547>. 
  Beyond the three built-in models (TD, RSTD, and Utility), users 
    can easily derive new variants by declaring which variables are 
    treated as free parameters.