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

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>.

Multi-category angle-based large-margin classifiers.
    See Zhang and Liu (2014) <doi:10.1093/biomet/asu017> for details.

An implementation of Karaboga (2005) Artificial Bee Colony
    Optimization algorithm <http://mf.erciyes.edu.tr/abc/pub/tr06_2005.pdf>.
    This (working) version is a Work-in-progress, which is
    why it has been implemented using pure R code. This was developed upon the basic
    version programmed in C and distributed at the algorithm's official website.

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”.

Performs Approximate Bayesian Computation (ABC) model choice and parameter inference via random forests.
    Pudlo P., Marin J.-M., Estoup A., Cornuet J.-M., Gautier M. and Robert C. P. (2016) <doi:10.1093/bioinformatics/btv684>.
    Raynal L., Marin J.-M., Pudlo P., Ribatet M., Robert C. P. and Estoup A. (2019) <doi:10.1093/bioinformatics/bty867>.

Offers methods to perform asymptotically bias-corrected regularized linear discriminant analysis (ABC_RLDA) for cost-sensitive binary classification. The bias-correction is an estimate of the bias term added to regularized discriminant analysis (RLDA) that minimizes the overall risk. The default magnitude of misclassification costs are equal and set to 0.5; however, the package also offers the options to set them to some predetermined values or, alternatively, take them as hyperparameters to tune.
    A. Zollanvari, M. Abdirash, A. Dadlani and B. Abibullaev (2019) <doi:10.1109/LSP.2019.2918485>.