Nonparametric smoothing methods for density and regression estimation involving circular data, 
				including the estimation of the mean regression function and other conditional characteristics.

Nonparametric tests for clustered data in pre-post intervention design documented in Cui and Harrar (2021) <doi:10.1002/bimj.201900310> and Harrar and Cui (2022) <doi:10.1016/j.jspi.2022.05.009>. Other than the main test results mentioned in the reference paper, this package also provides a function to calculate the sample size allocations for the input long format data set, and also a function for adjusted/unadjusted confidence intervals calculations. There are also functions to visualize the distribution of data across different intervention groups over time, and also the adjusted/unadjusted confidence intervals.

A non parametric test for change points detection in the dependence between the components of multivariate data, with or without (multiple) changes in the marginal distributions. The full details, justification and examples are published in Rohmer (2016) <doi:10.1016/j.spl.2016.06.026>.

An estimation procedure for the analysis of nonparametric proportional hazards model (e.g. h(t) = h0(t)exp(b(t)'Z)), providing estimation of b(t) and its pointwise standard errors, and semiparametric proportional hazards model (e.g. h(t) = h0(t)exp(b(t)'Z1 + c*Z2)), providing estimation of b(t), c and their standard errors. More details can be found in Lu Tian et al. (2005) <doi:10.1198/016214504000000845>.

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.