Package: LTAR
Type: Package
Title: Tensor Forecasting Functions
Version: 0.1.0
Authors@R: 
  c(person(given = "Kyle",
           family = "Caudle",
           role = c("aut", "cre"),
           email = "kyle.caudle@sdsmt.edu"),
    person(given = "Randy",
           family = "Hoover",
           role = "ctb"),
    person(given = "Jackson",
           family = "Cates",
           role = "ctb"))
Maintainer: Kyle Caudle <kyle.caudle@sdsmt.edu>
Description: A set of tools for forecasting the next step in a multidimensional setting using tensors.  In the examples, a forecast is made of sea surface temperatures of a geographic grid (i.e. lat/long).  Each observation is a matrix, the entries in the matrix and the sea surface temperature at a particular lattitude/longitude. Cates, J., Hoover, R. C., Caudle, K., Kopp, R., & Ozdemir, C. (2021) "Transform-Based Tensor Auto Regression for Multilinear Time Series Forecasting" in 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) (pp. 461-466), IEEE <doi:10.1109/ICMLA52953.2021.00078>.
Depends: R (>= 4.2.0)
Imports: vars,stats,rTensor, rTensor2, gsignal
License: GPL-3
Encoding: UTF-8
LazyData: true
Config/testthat/edition: 3
RoxygenNote: 7.2.3
NeedsCompilation: no
Packaged: 2023-08-21 14:08:48 UTC; kcaudle
Author: Kyle Caudle [aut, cre],
  Randy Hoover [ctb],
  Jackson Cates [ctb]
Repository: CRAN
Date/Publication: 2023-08-21 17:50:02 UTC
Built: R 4.2.0; ; 2023-08-22 11:36:56 UTC; unix
