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.

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>.

We provide functions to compute the nonparametric 
  maximum likelihood estimator (MLE) for 
  the bivariate distribution of (X,Y), when 
  realizations of (X,Y) cannot be observed directly. 
  To be more precise, we consider the situation 
  where we observe a set of rectangles in R^2 that are known 
  to contain the unobservable realizations of (X,Y). We
  compute the MLE based on such a set of rectangles. 
  The methods can also be used for univariate censored data (see data set
  'cosmesis'), and for 
  censored data with competing risks (see data set 'menopause'). 
  We also provide functions to visualize the observed data and the MLE.