Title: Neyman-Pearson Classification via Cost-Sensitive Learning
Description: We connect the multi-class Neyman-Pearson classification (NP) problem to the cost-sensitive learning (CS) problem, and propose two algorithms (NPMC-CX and NPMC-ER) to solve the multi-class NP problem through cost-sensitive learning tools. Under certain conditions, the two algorithms are shown to satisfy multi-class NP properties. More details are available in the paper "Neyman-Pearson Multi-class Classification via Cost-sensitive Learning" (Ye Tian and Yang Feng, 2021), which will be posted on arXiv soon.
Imports: dfoptim, nnet, randomForest, e1071, magrittr, MASS,
smotefamily, rpart, foreach, naivebayes, caret, formatR

Title: Nonparametric Estimation in Mixture Cure Models
Authors@R: c(person("Ignacio", "López-de-Ullibarri", role = c("aut", "cre"), email = "ignacio.lopezdeullibarri@udc.es"), person("Ana", "López-Cheda", role = "aut", email = "ana.lopez.cheda@udc.es"), person("Maria Amalia", "Jácome", role = "aut", email = "majacome@udc.es"))
Author: Ignacio López-de-Ullibarri [aut, cre],
Description: Performs nonparametric estimation in mixture cure models, and significance tests for the cure probability. For details, see López-Cheda et al. (2017a) <doi:10.1016/j.csda.2016.08.002> and López-Cheda et al. (2017b) <doi:10.1007/s11749-016-0515-1>.

Title: Normalised prediction distribution errors for nonlinear
Author: Emmanuelle Comets, Karl Brendel and France Mentr�
Description: Routines to compute normalised prediction distribution
errors, a metric designed to evaluate non-linear mixed effect
models such as those used in pharmacokinetics and

Title: Normalised prediction distribution errors for nonlinear mixed-effect models
Description: Routines to compute normalised prediction distribution errors, a metric designed to evaluate non-linear mixed effect models such as those used in pharmacokinetics and pharmacodynamics

Author: Emmanuelle Comets, Karl Brendel, Thi Huyen Tram Nguyen, France
Collate: 'global.R' 'NpdeData.R' 'NpdeRes.R' 'NpdeObject.R'