This package aims to provide classes and methods for
manipulating and processing of raster time series data (e.g. a
time series of satellite images).

methods, sp (>= 1.2-0), R (>= 3.0.0), raster, xts, zoo, RCurl

methods, sp (>= 1.4.1), zoo, RCurl, raster

Supports modelling real-time case data to facilitate the real-time
surveillance of infectious disease. A simple grid class structure is provided to generate a computational grid over
an area of interest with methods to map covariates between geographies. An approximate log-Gaussian Cox Process
model is fit using 'rstan' or 'cmdstanr' and provides output and analysis as 'sf' objects for simple visualisation.
'cmdstanr' can be downloaded at <https://mc-stan.org/cmdstanr/>. Log-Gaussian Cox Processes are described by
Diggle et al. (2013) <doi:10.1214/13-STS441> and we use the low-rank approximation  for Gaussian processes
described by Solin and Särkkä (2020) <doi:10.1007/s11222-019-09886-w> and Riutort-Mayol (2020) <arXiv:2004.11408>.

Supports modelling real-time case data to facilitate the real-time
surveillance of infectious disease. A simple grid class structure is provided to generate a computational grid over
an area of interest with methods to map covariates between geographies. An approximate log-Gaussian Cox Process
model is fit using 'rstan' or 'cmdstanr' and provides output and analysis as 'sf' objects for simple visualisation.
'cmdstanr' can be downloaded at <https://mc-stan.org/cmdstanr/>. Log-Gaussian Cox Processes are described by
Diggle et al. (2013) <doi:10.1214/13-STS441> and we provide both the low-rank approximation  for Gaussian processes
described by Solin and Särkkä (2020) <doi:10.1007/s11222-019-09886-w> and Riutort-Mayol et al (2020) <arXiv:2004.11408> and the
nearest neighbour Gaussian process described by Datta et al (2016) <doi:10.1080/01621459.2015.1044091>.

c(person("Johannes", "Mast", role = c("aut", "cre"), email = "johannes.mast@stud-mail.uni-wuerzburg.de", comment = c(ORCID = "0000-0001-6595-5834")),
person("Jakob", "Schwalb-Willmann", email = "movevis@schwalb-willmann.de", role = c("aut"), comment = c(ORCID = "0000-0003-2665-1509")))
A lightweight 'R' package to visualize large raster time series, building on a fast temporal interpolation core.
ggplot2, RStoolbox, moveVis, raster, stats, dplyr, pbapply,
sp, sf, tidyr, assertthat
Johannes Mast [aut, cre] (<https://orcid.org/0000-0001-6595-5834>),
Jakob Schwalb-Willmann [aut] (<https://orcid.org/0000-0003-2665-1509>)