Provides tools for likelihood-based inference in one-dimensional stochastic differential equations with mixed effects using expectation–maximization (EM) algorithms. The package supports Wiener and Ornstein–Uhlenbeck diffusion processes with user-specified drift functions, allowing flexible parametric forms including polynomial, exponential, and trigonometric structures. Estimation is performed via Markov chain Monte Carlo EM.

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.

Fits mixed membership models with discrete multivariate data (with
    or without repeated measures) following the general framework of Erosheva et al
    (2004). This package uses a Variational EM approach by approximating the
    posterior distribution of latent memberships and selecting hyperparameters
    through a pseudo-MLE procedure. Currently supported data types are
    Bernoulli, multinomial and rank (Plackett-Luce). The extended GoM model with 
    fixed stayers from Erosheva et al (2007) is now also supported. See Airoldi et al 
    (2014) for other examples of mixed membership models.

The estimation of the parameters in mixed Poisson models.