Contains functions for a flexible varying-coefficient landmark model by incorporating multiple short-term events into the prediction of long-term survival probability. For more information about landmark prediction please see Li, W., Ning, J., Zhang, J., Li, Z., Savitz, S.I., Tahanan, A., Rahbar.M.H., (2023+). "Enhancing Long-term Survival Prediction with Multiple Short-term Events: Landmarking with A Flexible Varying Coefficient Model".

Provides functions for landmark prediction of a survival outcome incorporating covariate and short-term event information. For more information about landmark prediction please see: Parast, Layla, Su-Chun Cheng, and Tianxi Cai. Incorporating short-term outcome information to predict long-term survival with discrete markers. Biometrical Journal 53.2 (2011): 294-307, <doi:10.1002/bimj.201000150>.

Processing of Landsat or other multispectral satellite imagery. Includes relative normalization, image-based radiometric correction, and topographic correction options. The original package description was published as Goslee (2011) <doi:10.18637/jss.v043.i04>, and details of the topographic corrections in Goslee (2012) <doi:10.14358/PERS.78.9.973>.

Calculates landscape metrics for categorical landscape patterns in 
    a tidy workflow. 'landscapemetrics' reimplements the most common metrics from
    'FRAGSTATS' (<https://www.fragstats.org/>) and new ones from the current 
    literature on landscape metrics. This package supports 'terra' SpatRaster objects 
    as input arguments. It further provides utility functions to visualize patches, 
    select metrics and building blocks to develop new metrics.

Simulates categorical maps on actual geographical realms, starting from either empty landscapes or landscapes provided by the user (e.g. land use maps). Allows to tweak or create landscapes while retaining a high degree of control on its features, without the hassle of specifying each location attribute. In this it differs from other tools which generate null or neutral landscapes in a theoretical space. The basic algorithm currently implemented uses a simple agent style/cellular automata growth model, with no rules (apart from areas of exclusion) and von Neumann neighbourhood (four cells, aka Rook case). Outputs are raster dataset exportable to any common GIS format.