Provides nonparametric CUSUM tests for detecting changes
  in possibly serially dependent univariate or low-dimensional
  multivariate observations. Retrospective tests sensitive to changes
  in the expectation, the variance, the covariance, the
  autocovariance, the distribution function, Spearman's rho, Kendall's
  tau, Gini's mean difference, and the copula are provided, as well as
  a test for detecting changes in the distribution of independent
  block maxima (with environmental studies in mind). The package also
  contains a test sensitive to changes in the autocopula and a
  combined test of stationarity sensitive to changes in the
  distribution function and the autocopula. The latest additions are
  an open-end sequential test based on the retrospective CUSUM
  statistic that can be used for monitoring changes in the mean of
  possibly serially dependent univariate observations, as well as
  closed-end and open-end sequential tests based on empirical
  distribution functions that can be used for monitoring changes in
  the contemporary distribution of possibly serially dependent
  univariate or low-dimensional multivariate observations.

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).

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>.

Performs nonparametric estimation in mixture cure models when the cure status is partially known. For details, see Safari et al (2021) <doi:10.1002/bimj.202100156>, Safari et al (2022) <doi:10.1177/09622802221115880> and Safari et al (2023) <doi:10.1007/s10985-023-09591-x>.