A fast implementation with additional experimental features for
             testing, monitoring and dating structural changes in (linear)
             regression models. 'strucchangeRcpp' features tests/methods from
	     the generalized fluctuation test framework as well as from
	     the F test (Chow test) framework. This includes methods to
             fit, plot and test fluctuation processes (e.g. cumulative/moving
             sum, recursive/moving estimates) and F statistics, respectively.
             These methods are described in Zeileis et al. (2002)
             <doi:10.18637/jss.v007.i02>.
             Finally, the breakpoints in regression models with structural
             changes can be estimated together with confidence intervals,
             and their magnitude as well as the model fit can be evaluated
             using a variety of statistical measures.

Spatial structural diversity refers to the spatial, i.e.
    horizontal arrangement of landscape elements and can reveal itself as
    landscape features, such as patches and linear features. The 'R' package
    'StrucDiv' provides methods to quantify spatial structural diversity
    in continuous remote sensing data, or in other data in raster format.
    Structure is based on the spatial arrangement of value pairs. The 'R' package 'StrucDiv'
    includes methods to combine information from different spatial scales, which allows to quantify
    multi-scale spatial structural diversity.

Tree-structured modelling of categorical predictors (Tutz and Berger (2018), <doi:10.1007/s11634-017-0298-6>) or measurement
    units (Berger and Tutz (2018), <doi:10.1080/10618600.2017.1371030>).

Explains the behavior of a time series by decomposing it into its trend, seasonality and residuals. 
             It is built to perform very well in the presence of significant level shifts. It is designed to play 
             well with any breakpoint algorithm and any smoothing algorithm. Currently defaults to 'lowess' for smoothing
             and 'strucchange' for breakpoint identification. The package is useful in areas such as trend analysis, time series 
             decomposition, breakpoint identification and anomaly detection.