Description: It provides the density, distribution function, quantile function, random number generator, reliability function, failure rate, likelihood function,
moments and EM algorithm for Maximum Likelihood estimators, also empirical quantile and generated envelope for a given sample, all this for the three parameter
Birnbaum-Saunders model based on Skew-Normal Distribution.
Additionally, it provides the random number generator for the mixture of Birnbaum-Saunders model based on Skew-Normal distribution.

Author: Luis Benites Sanchez[cre, aut] <lbenitess@pucp.edu.pe>, Rocio Paola Maehara[cre, aut] <rp.maeharaa@up.edu.pe> and Paulo Jos<c3><a9> Alejandro Aybar Flores[ctb] <a.aybarf@alum.up.edu.pe>
Maintainer: Rocio Paola Maehara <rp.maeharaa@up.edu.pe>
Description: It provides the density, distribution function, quantile function, random number generator, reliability function, failure rate, likelihood function, moments and EM algorithm for Maximum Likelihood estimators, also empirical quantile and generated envelope for a given sample, all this for the three parameter Birnbaum-Saunders model based on Skew-Normal Distribution. Also, it provides the random number generator for the mixture of Birnbaum-Saunders model based on Skew-Normal distribution. Additionally, we incorporate the EM algorithm based on the assumption that the error term follows a finite mixture of Sinh-normal distributions.

Description: The package contains HingeBoost for binary and multi-class
classification with unequal misclassification costs. Functional
gradient descent algorithm to optimize the cost-sensitive hinge
loss. The algorithm can fit linear classifier with linear least
squares, nonlinear classifer with smoothing spline (or trees)
as base learner. The package also can conduct regression.

Authors@R: c(person("Zhu", "Wang", role = c("aut", "cre"),
person("Torsten", "Hothorn", role = "ctb"))
Description: Functional gradient descent algorithm for a variety of convex and nonconvex loss functions, for both classical and robust regression and classification problems. HingeBoost is implemented for binary and multi-class classification, with unequal misclassification costs for binary case. The algorithm can fit linear and nonlinear classifiers.
Imports: rpart, methods, foreach, doParallel