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.

Pseudo-random number generation for 11 multivariate distributions: Normal, t, Uniform, Bernoulli, Hypergeometric, Beta (Dirichlet), Multinomial, Dirichlet-Multinomial, Laplace, Wishart, and Inverted Wishart. The details of the method are explained in Demirtas (2004) <DOI:10.22237/jmasm/1099268340>.

Multiply robust estimation for population mean (Han and Wang 2013) <doi:10.1093/biomet/ass087>, regression analysis (Han 2014) <doi:10.1080/01621459.2014.880058> (Han 2016) <doi:10.1111/sjos.12177> and quantile regression (Han et al. 2019) <doi:10.1111/rssb.12309>.

Implementation of a next-generation, multi-stock age-structured fisheries assessment model. 
  'multiSA' is intended for use in mixed fisheries where stock composition can not be readily identified in 
  fishery data alone, e.g., from catch and age/length composition. Models can be fitted to genetic data, 
  e.g., stock composition of catches and close-kin pairs, with seasonal stock availability and movement.