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) <arXiv:2010.09154>.

Miscellaneous scripts, e.g. functionality to make and plot factor diagrams for the statistical design.

A variety of ordination and community analyses
   useful in analysis of data sets in community ecology.  
   Includes many of the common ordination methods, with 
   graphical routines to facilitate their interpretation, 
   as well as several novel analyses.

The Bayesian estimation of mixture models (and more general hidden Markov models) suffers from the label switching phenomenon, making the MCMC output non-identifiable. This package can be used in order to deal with this problem using various relabelling algorithms.