Extends the fitdistr() function (of the MASS package) with several functions 
  to help the fit of a parametric distribution to non-censored or censored data. 
  Censored data may contain left censored, right censored and interval censored values, 
  with several lower and upper bounds. In addition to maximum likelihood estimation (MLE), 
  the package provides moment matching (MME), quantile matching (QME), maximum goodness-of-fit 
  estimation (MGE) and maximum spacing estimation (MSE) methods (available only for 
  non-censored data). Weighted versions of MLE, MME, QME and MSE are available. See e.g. 
  Casella & Berger (2002), Statistical inference, Pacific Grove, for a general introduction 
  to parametric estimation.

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>.