Strings are abbreviated to at least "minlength" characters, such that they remain unique 
    (if they were). The abbreviations should be recognisable.

Implements several ABC algorithms for
        performing parameter estimation, model selection, and goodness-of-fit.
        Cross-validation tools are also available for measuring the
        accuracy of ABC estimates, and to calculate the
        misclassification probabilities of different models.

Contains data which are used by functions of the 'abc' package.

It aims to identify candidate genes that are “differentially
    methylated” between cases and controls. It applies Student’s t-test and delta beta analysis to
    identify candidate genes containing multiple “CpG sites”.

For a given data set, the package provides a novel method of computing precise limits to acquire subsets which are easily interpreted. Closely related to the Lorenz curve, the ABC curve visualizes the data by graphically representing the cumulative distribution function. Based on an ABC analysis the algorithm calculates, with the help of the ABC curve, the optimal limits by exploiting the mathematical properties pertaining to distribution of analyzed items. The data containing positive values is divided into three disjoint subsets A, B and C, with subset A comprising very profitable values, i.e. largest data values ("the important few"), subset B comprising values where the yield equals to the effort required to obtain it, and the subset C comprising of non-profitable values, i.e., the smallest data sets ("the trivial many"). Package is based on "Computed ABC Analysis for rational Selection of most informative Variables in multivariate Data", PLoS One. Ultsch. A., Lotsch J. (2015) <DOI:10.1371/journal.pone.0129767>.

Provides functions to compute summary scores
  (besides proprietary ones) reported in the tabulated data resource that is
  released by the Adolescent Brain Cognitive Development (ABCD) study.

Empirical likelihood-based approximate Bayesian Computation.  Approximates the required posterior using empirical likelihood and estimated differential entropy.  This is achieved without requiring any specification of the likelihood or estimating equations that connects the observations with the underlying parameters. The procedure is known to be posterior consistent.  More details can be found in Chaudhuri, Ghosh, and Kim (2024) <doi:10.1002/SAM.11711>.