Difference scaling is a method for scaling perceived 
  supra-threshold differences.  The package contains functions that
  allow the user to design and run a difference scaling experiment, 
  to fit the resulting data by maximum likelihood and test the
  internal validity of the estimated scale.

Several functions for maximum likelihood estimation of various univariate and multivariate distributions. The list includes more than 100 univariate continuous and discrete distributions, distributions that lie on the real line, the positive line, interval restricted, circular distributions. Further, multivariate continuous and discrete distributions, distributions for compositional and directional data, etc. Some references include Johnson N. L., Kotz S. and Balakrishnan N. (1994). "Continuous Univariate Distributions, Volume 1" <ISBN:978-0-471-58495-7>, Johnson, Norman L. Kemp, Adrianne W. Kotz, Samuel (2005). "Univariate Discrete Distributions". <ISBN:978-0-471-71580-1> and Mardia, K. V. and Jupp, P. E. (2000). "Directional Statistics". <ISBN:978-0-471-95333-3>.

Calculates the expected/observed Fisher information and the bias-corrected maximum likelihood estimate(s) via Cox-Snell Methodology.

A unified interface is provided to various machine learning
  algorithms like linear or quadratic discriminant analysis, k-nearest
  neighbors, random forest, support vector machine, ... It allows to train,
  test, and apply cross-validation using similar functions and function
  arguments with a minimalist and clean, formula-based interface. Missing data
  are processed the same way as base and stats R functions for all algorithms,
  both in training and testing. Confusion matrices are also provided with a rich
  set of metrics calculated and a few specific plots.

Asymptotic efficient closed-form estimators (MLEces) are provided in this package for three multivariate distributions(gamma, Weibull and Dirichlet) whose maximum likelihood estimators (MLEs) are not in closed forms. Closed-form estimators are strong consistent, and have the similar asymptotic normal distribution like MLEs. But the calculation of MLEces are much faster than the corresponding MLEs. Further details and explanations of MLEces can be found in.
    Jang, et al. (2023) <doi:10.1111/stan.12299>.
    Kim, et al. (2023) <doi:10.1080/03610926.2023.2179880>.