Estimation of a dynamic lognormal - Generalized Pareto mixture via the Approximate Maximum Likelihood and the Cross-Entropy methods. See Bee, M. (2023) <doi:10.1016/j.csda.2023.107764>.

Robust estimation methods for the mean vector, scatter matrix,
    and covariance matrix (if it exists) from data (possibly containing NAs) 
    under multivariate heavy-tailed distributions such as angular Gaussian 
    (via Tyler's method), Cauchy, and Student's t distributions. Additionally, 
    a factor model structure can be specified for the covariance matrix. The
    latest revision also includes the multivariate skewed t distribution.
    The package is based on the papers: Sun, Babu, and Palomar (2014);
    Sun, Babu, and Palomar (2015); Liu and Rubin (1995);
    Zhou, Liu, Kumar, and Palomar (2019); Pascal, Ollila, and Palomar (2021).

A toolbox for estimating vector fields from intensive
    longitudinal data, and construct potential landscapes thereafter. The
    vector fields can be estimated with two nonparametric methods: the
    Multivariate Vector Field Kernel Estimator (MVKE) by Bandi & Moloche
    (2018) <doi:10.1017/S0266466617000305> and the Sparse Vector Field
    Consensus (SparseVFC) algorithm by Ma et al.  (2013)
    <doi:10.1016/j.patcog.2013.05.017>. The potential landscapes can be
    constructed with a simulation-based approach with the 'simlandr'
    package (Cui et al., 2021) <doi:10.31234/osf.io/pzva3>, or the
    Bhattacharya et al. (2011) method for path integration
    <doi:10.1186/1752-0509-5-85>.

Fits the lifespan datasets of biological systems such as yeast, fruit flies, and other similar biological units with well-known finite mixture models introduced by Farewell V. (1982) <doi:10.2307/2529885> and Al-Hussaini et al. (2000) <doi:10.1080/00949650008812033>. Estimates parameter space fitting of a lifespan dataset with finite mixtures of parametric distributions. Computes the following tasks; 1) Estimates parameter space of the finite mixture model 
             by implementing the expectation maximization (EM) algorithm. 2) Finds a sequence of four goodness-of-fit measures consist of Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Kolmogorov-Smirnov (KS), and log-likelihood (log-likelihood) statistics. 3)The initial values is determined by k-means clustering.