c(person("Jean-Michel Marin", role=c("aut", "cre"), email="jean-michel.marin@umontpellier.fr"),
person("Louis Raynal", role="aut", email="louis.raynal@umontpellier.fr"),
person("Pierre Pudlo",  role="aut", email="pierre.pudlo@umontpellier.fr"),
person("Christian P. Robert", role="ctb"),
person("Arnaud Estoup", role="ctb") )
Jean-Michel Marin [aut, cre],
Louis Raynal [aut],
Pierre Pudlo [aut],
Christian P. Robert [ctb],
Arnaud Estoup [ctb]

readr, MASS, matrixStats, ranger, parallel, stringr, Rcpp (>=
0.11.2)

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. (2017) <arXiv:1605.05537>.

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>.
Estoup A., Raynal L., Verdu P. and Marin J.-M. <http://journal-sfds.fr/article/view/709>.
Raynal L., Marin J.-M., Pudlo P., Ribatet M., Robert C. P. and Estoup A. (2019) <doi:10.1093/bioinformatics/bty867>.
readr, MASS, matrixStats, ranger, doParallel, parallel,
foreach, stringr, Rcpp (>= 0.11.2)

Asymptotically Bias-Corrected Regularized Linear Discriminant
Analysis
Dmitriy Fedorov [aut, cre],
Amin Zollanvari [aut],
Aresh Dadlani [aut],
Berdakh Abibullaev [aut]
c(
person("Dmitriy", "Fedorov", email = "dmitriy.fedorov@nu.edu.kz", role = c("aut", "cre")),
person("Amin", "Zollanvari", email = "amin.zollanvari@nu.edu.kz", role = "aut"),
person("Aresh", "Dadlani", email = "aresh.dadlani@nu.edu.kz ", role = "aut"),
person("Berdakh", "Abibullaev", email = "berdakh.abibullaev@nu.edu.kz", role = "aut"))
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>.