A set of tools for creation, manipulation, and modeling
of tensors with arbitrary number of modes. A tensor in the context of data
analysis is a multidimensional array. rTensor does this by providing a S4
class 'Tensor' that wraps around the base 'array' class. rTensor
provides common tensor operations as methods, including matrix unfolding,
summing/averaging across modes, calculating the Frobenius norm, and taking
the inner product between two tensors. Familiar array operations are
overloaded, such as index subsetting via '[' and element-wise operations.
rTensor also implements various tensor decomposition, including CP, GLRAM,
MPCA, PVD, and Tucker. For tensors with 3 modes, rTensor also implements
transpose, t-product, and t-SVD, as defined in Kilmer et al. (2013). Some
auxiliary functions include the Khatri-Rao product, Kronecker product, and
the Hadamard product for a list of matrices.

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>.
methods,rTensor, wavethresh, gsignal, Matrix, matrixcalc

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.

c(
person("Alfonso R.", "Reyes", role = c("aut", "cre", "cph"), email = "alfonso.reyes@oilgainsanalytics.com"),
person("Daniel", "Falbel", role = c("ctb", "cph"), email = "daniel@rstudio.com"),
person("JJ", "Allaire", role = c("ctb", "cph"))
)
'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.
logging, reticulate, jsonlite (>= 1.2), utils, methods, R6,
rstudioapi (>= 0.7), data.table
Alfonso R. Reyes [aut, cre, cph],
Daniel Falbel [ctb, cph],
JJ Allaire [ctb, cph]