spatstat, leaflet, shiny, sp, raster, maptools, rgdal,
shinythemes, prodlim

spatstat.geom, spatstat.core, spatstat.linnet, spatstat (>=
2.0-0), leaflet, shiny, sp, raster, maptools, rgdal,
shinythemes, prodlim

Nonparametric Modeling and Monitoring of Spatio-Temporal Data
c(person("Kai",  "Yang", role = c("aut", "cre"), email = "kayang@mcw.edu"),
person("Peihua", "Qiu", role = "ctb"))
Spatio-temporal data have become increasingly popular in many research fields. Such data often have complex structures that are difficult to describe and estimate. This package provides reliable tools for modeling complicated spatio-temporal data. It also includes tools of online process monitoring to detect possible change-points in a spatio-temporal process over time. More specifically, the package implements the spatio-temporal mean estimation procedure described in Yang and Qiu (2018) <doi:10.1002/sim.7622>, the spatio-temporal covariance estimation procedure discussed in Yang and Qiu (2019) <doi:10.1002/sim.8315>, the three-step method for the joint estimation of spatio-temporal mean and covariance functions suggested by Yang and Qiu (2022) <doi:10.1007/s10463-021-00787-2>, the spatio-temporal disease surveillance method discussed in Qiu and Yang (2021) <doi:10.1002/sim.9150> that can accommodate the covariate effect, the spatial-LASSO-based process monitoring method proposed by Qiu and Yang (2022) <doi:10.1080/00224065.2022.2081104>, and the online spatio-temporal disease surveillance method described in Yang and Qiu (2020) <doi:10.1080/24725854.2019.1696496>.

Genomic Region Detection via Spatial Convex Clustering. See <https://arxiv.org/abs/1611.04696> for details.
abind, dplyr, ggplot2, parallel, Rcpp, tidyr

Develop and Run Spatially Explicit Discrete Event Simulation
Models
Easily implement a variety of simulation models, with a focus on
spatially explicit agent based models. These include raster-based,
event-based, and agent-based models. The core simulation components are
built upon a discrete event simulation framework that facilitates
modularity, and easily enables the user to include additional functionality
by running user-built simulation modules. Included are numerous tools to
visualize raster and other maps. The suggested package `fastshp` can be
installed with `install.packages("fastshp", repos="http://rforge.net",
type="source")`.
c(
person(c("Alex", "M"), "Chubaty", email="achubaty@NRCan.gc.ca",
role=c("aut", "cre")),
person(c("Eliot", "J", "B"), "McIntire", email="emcintir@NRCan.gc.ca",
role=c("aut")),
person("Steve", "Cumming", email="Steve.Cumming@sbf.ulaval.ca",
role=c("ctb")),
person("Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources Canada",
role="cph")
)
fastshp, knitr, Matrix, RColorBrewer, rgdal, testthat,
tkrplot
archivist, CircStats, data.table, DiagrammeR, digest,
downloader, dplyr, ff, ffbase, fpCompare, ggplot2, graphics,
grid, gridBase, httr, igraph, lubridate, methods, R.utils,
RandomFields, raster, secr, sp, stringi, stringr
'agent.R' 'SELES.R' 'module-dependencies-class.R'
'misc-methods.R' 'simList-class.R' 'check.R' 'environment.R'
'checkpoint.R' 'initialize.R' 'simulation.R' 'load.R'
'mapReduce.R' 'module-dependencies-methods.R'
'module-repository.R' 'module-template.R' 'moduleMetadata.R'
'movement.R' 'neighbourhood.R' 'numerical-comparisons.R'
'plotting-classes.R' 'plotting-colours.R' 'times.R'
'simList-accessors.R' 'plotting-diagrams.R'
'plotting-helpers.R' 'plotting-other.R' 'plotting.R'
'probability.R' 'progress.R' 'save.R' 'spades-classes.R'
'spades-package.R' 'splitRaster.R' 'spread-process.R' 'zzz.R'
Alex M Chubaty [aut, cre],
Eliot J B McIntire [aut],
Steve Cumming [ctb],
Her Majesty the Queen in Right of Canada, as represented by the
Minister of Natural Resources Canada [cph]