Neyman-Pearson Classification via Cost-Sensitive Learning
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.
dfoptim, nnet, randomForest, e1071, magrittr, MASS,
smotefamily, rpart, foreach, naivebayes, caret, formatR

Nonparametric Estimation in Mixture Cure Models
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"))
Ignacio López-de-Ullibarri [aut, cre],
Ana López-Cheda [aut],
Maria Amalia Jácome [aut]
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>.

Normalised prediction distribution errors for nonlinear
mixed-effect models
Emmanuelle Comets, Karl Brendel and France Mentré
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

Normalised prediction distribution errors for nonlinear mixed-effect models
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

Emmanuelle Comets, Karl Brendel, Thi Huyen Tram Nguyen, France
Mentre.
'global.R' 'NpdeData.R' 'NpdeRes.R' 'NpdeObject.R'
'func_methods.R' 'func_plots.R' 'main.R' 'zzz.R'