Semi-parametric approach for sparse canonical correlation analysis 
    which can handle mixed data types: continuous, binary and truncated continuous.
    Bridge functions are provided to connect Kendall's tau to latent correlation
    under the Gaussian copula model. The methods are described in 
    Yoon, Carroll and Gaynanova (2020) <doi:10.1093/biomet/asaa007> and 
    Yoon, Mueller and Gaynanova (2021) <doi:10.1080/10618600.2021.1882468>.

Generates blocked designs for mixed-level factorial experiments for a 
    given block size. Internally, it uses finite-field based, collapsed, and heuristic 
    methods to construct block structures that minimize confounding between block 
    effects and factorial effects. The package creates the full treatment combination 
    table, partitions runs into blocks, and computes detailed confounding diagnostics 
    for main effects and two-factor interactions. It also checks orthogonal factorial 
    structure (OFS) and computes efficiencies of factorial effects using the methods 
    of Nair and Rao (1948) <doi:10.1111/j.2517-6161.1948.tb00005.x>. When OFS is not satisfied but 
    the design has equal treatment replications and equal block sizes, a general method 
    based on the C-matrix and custom contrast vectors is used to compute efficiencies. 
    The output includes the generated design, finite-field metadata, confounding 
    summaries, OFS diagnostics, and efficiency results.

Functions for testing randomness for a univariate time series with arbitrary distribution  (discrete, continuous, mixture of both types) and for testing  independence between random variables with arbitrary distributions. The test statistics are based on the multilinear empirical copula and multipliers are used to compute P-values. The test of independence between random variables appeared in  Genest, Nešlehová, Rémillard & Murphy (2019) and the test of randomness appeared in Nasri (2022).

Mixed, low-rank, and sparse multivariate regression ('mixedLSR') provides tools for performing mixture regression when 
  the coefficient matrix is low-rank and sparse. 'mixedLSR' allows subgroup identification by alternating optimization 
  with simulated annealing to encourage global optimum convergence. This method is data-adaptive, automatically 
  performing parameter selection to identify low-rank substructures in the coefficient matrix.