Changes of landscape diversity and structure can be detected
    soon if relying on landscape class combinations and analysing patterns
    at multiple scales. 'LandComp' provides such an opportunity, based on
    Juhász-Nagy's functions (Juhász-Nagy P, Podani J 1983
    <doi:10.1007/BF00129432>). Functions can handle multilayered data.
    Requirements of the input: binary data contained by a regular square
    or hexagonal grid, and the grid should have projected coordinates.

Provides functions to estimate survival and a treatment effect using a landmark estimation approach.

Provides a function for classifying a landscape into different categories based on the Topographic Position Index (TPI) and slope. It offers two types of classifications: Slope Position Classification, and Landform Classification. The function internally calculates the TPI for the given landscape and then uses it along with the slope to perform the classification. Optionally, descriptive statistics for every class are calculated and plotted. The classifications are useful for identifying the position of a location on a slope and for identifying broader landform types.

The landmark approach allows survival predictions to be
	updated dynamically as new measurements from an individual are recorded.
	The idea is to set predefined time points, known as "landmark times",
	and form a model at each landmark time using only the individuals in the
	risk set. This package allows the longitudinal data to be modelled
	either using the last observation carried forward or linear mixed
	effects modelling. There is also the option to model competing risks,
	either through cause-specific Cox regression or Fine-Gray regression.
	To find out more about the methods in this package, please see 
	<https://isobelbarrott.github.io/Landmarking/articles/Landmarking>.

Non-parametric prediction of survival outcomes for mixture data that incorporates covariates and a landmark time. Details are described in Garcia (2021) <doi:10.1093/biostatistics/kxz052>.