L1 estimation for linear regression using Barrodale and Roberts' method
    <doi:10.1145/355616.361024> and the EM algorithm <doi:10.1023/A:1020759012226>.
    Estimation of mean and covariance matrix using the multivariate Laplace distribution,
    density, distribution function, quantile function and random number generation
    for univariate and multivariate Laplace distribution <doi:10.1080/03610929808832115>.

Provides an l1-version of the spectral clustering algorithm devoted to robustly clustering highly perturbed graphs using l1-penalty. This algorithm is described with more details in the preprint C. Champion, M. Champion, M. Blazère, R. Burcelin and J.M. Loubes, "l1-spectral clustering algorithm: a spectral clustering method using l1-regularization" (2022).

Efficient implementation of Friedman's boosting algorithm with
    l2-loss function and coordinate direction (design matrix columns) basis
    functions.

Provides functions for the implementation of a density goodness-of-fit test, based on piecewise approximation of the L2 distance.

An implementation of a computational framework for performing robust structured regression with the L2 criterion 
             from Chi and Chi (2021+). Improvements using the majorization-minimization (MM) principle from Liu, Chi, and 
             Lange (2022+) added in Version 2.0.

Provides a method for detecting multiple change points in high-dimensional time
    series, targeting dense or spatially clustered signals. See Li et al. (2023) 
    "L2 Inference for Change Points in High-Dimensional Time Series via a Two-Way MOSUM". 
    arXiv preprint <arXiv:2208.13074>.

Contains Lioness Algorithm (LA) for finding optimal designs over continuous design space, optimal Latin hypercube designs, and optimal order-of-addition designs. LA is a brand new nature-inspired meta-heuristic optimization algorithm. Detailed methodologies of LA and its implementation on numerical simulations can be found at Hongzhi Wang, Qian Xiao and Abhyuday Mandal (2021) <doi:10.48550/arXiv.2010.09154>.