Core functions and the data architecture required for prediction and inference of spatiotemporal models: kernal-based lattice models that divide and conquer large space-time modelling and prediction problems. A local space-time operator/filter is used with a global covariate model. It is therefore, an hierarchical model.
To install you need to bootstrap from https://github.com/jae0/aegis directly:
devtools::install_github( "jae0/aegis" )
Then, you need to have an Rprofile set up properly. An example can be seen in aegis/R/project.Rprofile.example.R, or use the following, being careful to define the required R-global variables:
libPaths("~/R") homedir = path.expand("~") tmpdir = file.path( homedir, "tmp" ) work_root = file.path( homedir, "work" ) ### replace with correct path to work directory (local temporary storage) code_root = file.path( homedir, "bio" ) ### replace with correct path to the parent directory of your git-projects data_root = file.path( homedir, "bio.data" ) ### replace with correct path to your data # store your passwords and login here and make sure they are secure passwords = file.path( homedir, ".passwords" ) if (file.exists(passwords)) source( passwords ) require( aegis )
Thereafter, you can used the bootstrapped environment to install the other basic tools:
If you have a local git clone of the required packages, you can install with:
For usage, examples can be found in https://github.com/jae0/aegis.*.
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