c(
person(given = "Tanya",
family = "Garcia",
role = c("aut"),
email = "tpgarcia16@gmail.com"),
person("Layla", "Parast", email = "parast@austin.utexas.edu",
role = "cre"))
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>.

Landmark Prediction of a Survival Outcome
This package includes 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.

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>.

Radiometric and topographic correction of satellite imagery
Processing of Landsat or other multispectral satellite
imagery. Includes relative normalization, image-based
radiometric correction, and topographic correction options.

Radiometric and Topographic Correction of Satellite Imagery
Processing of Landsat or other multispectral satellite imagery. Includes relative normalization, image-based radiometric correction, and topographic correction options.

Landsat 8 Imagery Rescaled to Reflectance, Radiance and/or
Temperature
Functions for converted Landsat 8 multispectral satellite imagery
rescaled to the top of atmosphere (TOA) reflectance, radiance
and/or at satellite brightness temperature using radiometric
rescaling coefficients provided in the metadata file (MTL file).

This set of functions allows the simulation of categorical maps from scratch in a geographically referenced landscape or the manipulation of existing ones. The basic algorithm currently implemented uses a simple agent style/cellular automata growth model, with no rules (apart from areas of exclusion), therefore expands more or less circularly picking cells at the edges randomly.