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.

Provides a compact C++ backend for Approximate Bayesian
    Computation (ABC) with a thin R frontend. The current implementation is
    primarily a C++ reimplementation of offline ABC workflows provided by
    the R 'abc' package <doi:10.32614/CRAN.package.abc>, with the public R
    interface intentionally kept small and centered on abc() and summary()
    methods. The computational work is performed by shared C++ code. In addition
    to reproducing common 'abc' workflows, the package adds optional
    dimensionality reduction of summary statistics through Principal
    Component Analysis (PCA) and Partial Least Squares (PLS), following
    related ideas described by Bazin et al. (2010)
    <doi:10.1534/genetics.109.112391> and Wegmann et al. (2009)
    <doi:10.1534/genetics.109.102509>.

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>.