Detection of change-points for variance of heteroscedastic Gaussian variables with piecewise constant variance function. Adelfio, G. (2012), Change-point detection for variance piecewise constant models, Communications in Statistics, Simulation and Computation, 41:4, 437-448, <doi:10.1080/03610918.2011.592248>.

A basic implementation of the change in mean detection method outlined in: Taylor, Wayne A. (2000) <https://variation.com/wp-content/uploads/change-point-analyzer/change-point-analysis-a-powerful-new-tool-for-detecting-changes.pdf>. The package recursively uses the mean-squared error change point calculation to identify candidate change points. The candidate change points are then re-estimated and Taylor's backwards elimination process is then employed to come up with a final set of change points. Many of the underlying functions are written in C++ for improved performance.

A multiple testing procedure for clustered alternative hypotheses. 
    It is assumed that the p-values under the null hypotheses follow U(0,1) and 
    that the distributions of p-values from the alternative hypotheses are 
    stochastically smaller than U(0,1). By aggregating information, this 
    method is more sensitive to detecting signals of low magnitude than 
    standard methods. Additionally, sporadic small p-values appearing 
    within a null hypotheses sequence are avoided by averaging on the 
    neighboring p-values.

Change point tests for joint distributions and copulas using pseudo-observations with multipliers or bootstrap. The processes used here have been defined in Bucher, Kojadinovic, Rohmer & Segers <doi:10.1016/j.jmva.2014.07.012> and Nasri & Remillard <doi:10.1016/j.jmva.2019.03.002>.

Changing the name of an existing R package is annoying but common task especially in the early stages of package development. This package (mostly) automates this task.

Estimation of changepoints using an "S-curve"
   approximation. Formation of confidence intervals for changepoint
   locations and magnitudes. Both abrupt and gradual changes can be
   modeled.