R Codes and Datasets for Stroup, W. W. (2012). Generalized Linear Mixed Models: Modern Concepts, Methods and Applications, CRC Press.

Testing, monitoring and dating structural changes in (linear)
             regression models. strucchange 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., CUSUM, MOSUM,
	     recursive/moving estimates) and F statistics, respectively.
             It is possible to monitor incoming data online using
             fluctuation processes.
             Finally, the breakpoints in regression models with structural
             changes can be estimated together with confidence intervals.
             Emphasis is always given to methods for visualizing the data.

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.