Provides the core framework for a discrete event system to 
    implement a complete data-to-decisions, reproducible workflow.
    The core components facilitate the development of modular pieces, 
    and enable the user to include additional functionality by running user-built modules.
    Includes conditional scheduling, restart after interruption, packaging of
    reusable modules, tools for developing arbitrary automated workflows,
    automated interweaving of modules of different temporal resolution,
    and tools for visualizing and understanding the within-project dependencies. 
    The suggested package 'NLMR' can be installed from the repository 
    (<https://PredictiveEcology.r-universe.dev>).

Provides GIS and map utilities, plus additional modeling tools for
    developing cellular automata, dynamic raster models, and agent based models
    in 'SpaDES'.
    Included are various methods for spatial spreading, spatial agents, GIS operations,
    random map generation, and others.
    See '?SpaDES.tools' for an categorized overview of these additional tools.
    The suggested package 'NLMR' can be installed from the following repository:
    (<https://PredictiveEcology.r-universe.dev>).

Implements methods for anticipating the emergence and eradication
    of infectious diseases from surveillance time series. Also provides support
    for computational experiments testing the performance of such methods.

Set of functions for sparse matrix algebra.
    Differences with other sparse matrix packages are:
    (1) we only support (essentially) one sparse matrix format,
    (2) based on transparent and simple structure(s),
    (3) tailored for MCMC calculations within G(M)RF.
    (4) and it is fast and scalable (with the extension package spam64).
    Documentation about 'spam' is provided by vignettes included in this package, see also Furrer and Sain (2010) <doi:10.18637/jss.v036.i10>; see 'citation("spam")' for details.

Provides the Fortran code of the R package 'spam'
    with 64-bit integers. Loading this package together with the R package
    spam enables the sparse matrix class spam to handle huge sparse matrices
    with more than 2^31-1 non-zero elements.
    Documentation is provided in Gerber, Moesinger and Furrer (2017) <doi:10.1016/j.cageo.2016.11.015>.