Description: A set of tools for basic tensor operators.  A tensor in the context of data analysis in a multidimensional array. The tools in this package rely on using any discrete transformation (e.g. Fast Fourier Transform (FFT)).  Standard tools included are the Eigenvalue decomposition of a tensor, the QR decomposition and LU decomposition.  Other functionality includes the inverse of a tensor and the transpose of a symmetric tensor. Functionality in the package is outlined in Kernfeld et al. (2015) <https://www.sciencedirect.com/science/article/pii/S0024379515004358>.
Imports: methods,rTensor, wavethresh, gsignal, Matrix, matrixcalc

Description: Models and displays tephra transport through custom (windy, turbulent, heterogeneous) atmosphere over custom topography. Includes a Lagrangian (particle-tracking) tephra transport model and a function to save snapshots of model as png files.

person("Alfonso R.", "Reyes", role = c("aut", "cre", "cph"), email = "alfonso.reyes@oilgainsanalytics.com"),
person("JJ", "Allaire", role = c("ctb", "cph"))
Description: 'R' implementation and interface of the Machine Learning platform
'PyTorch' <https://pytorch.org/> developed in 'Python'. It requires a 'conda'
environment with 'torch' and 'torchvision' to provide 'PyTorch' functions,
methods and classes. The key object in 'PyTorch' is the tensor which is in
essence a multidimensional array. These tensors are fairly flexible to perform
calculations in CPUs as well as 'GPUs' to accelerate the process.
Imports: logging, reticulate, jsonlite (>= 1.2), utils, methods, R6,
Author: Alfonso R. Reyes [aut, cre, cph],

person("Alfonso R.", "Reyes", role = c("aut", "cre", "cph"), email = "alfonso.reyes@oilgainsanalytics.com")
Imports: reticulate (>= 1.10), jsonlite (>= 1.2), utils, methods,
SystemRequirements: conda (python=3.6 pytorch torchvision cpuonly
Author: Alfonso R. Reyes [aut, cre, cph]

environment with 'torch' and 'torchvision' Python packages to provide
'PyTorch' functions, methods and classes. The key object in 'PyTorch' is the
tensor which is in essence a multidimensional array. These tensors are fairly
flexible in performing calculations in CPUs as well as 'GPUs' to accelerate
SystemRequirements: "conda (python>=3.6 pytorch torchvision cpuonly
numpy ( >= 1.14.0) matplotlib pandas -c pytorch);
python-minimal, pandoc pandoc-citeproc, qpdf; Python (>=3.6),
pytorch (>=1.4), torchvision, cpuonly, numpy ( >= 1.14.0);
pandoc (>= 2.0), qpdf ( >= 7.0) on Solaris"