Simulation of positive definite matrices constrained by an undirected or acyclic directed graph structure.

Simulation of Graphical Correlation Matrices
Simulation of correlation matrices possibly constrained by a given undirected or acyclic directed graph. In particular, the package provides functions that implement the simulation methods described in Córdoba et al. (2018a) <doi:10.1007/978-3-030-03493-1_13>, Córdoba et al. (2018b) <doi:10.1145/2695664.2695717> and Córdoba et al. (2019) <arXiv:1909.01062>.

c(
person(given = "Irene", family = "Córdoba", role = "aut",
comment = c(ORCID = "0000-0002-3252-4234")),
person(given = "Gherardo", family = "Varando", email = "gherardo.varando@math.ku.dk", role = c("aut", "cre"),
comment = c(ORCID = "0000-0002-6708-1103")),
person(given = "Concha", family = "Bielza", role = "ths",
comment = c(ORCID = "0000-0001-7109-2668")),
person(given = "Pedro", family = "Larrañaga", role = "ths",
comment = c(ORCID = "0000-0003-0652-9872")))
Simulation of correlation matrices possibly constrained by a given undirected or acyclic directed graph. In particular, the package provides functions that implement the simulation methods described in Córdoba et al. (2018) <doi:10.1007/978-3-030-03493-1_13> and Córdoba et al. (2020) <doi:10.1016/j.ijar.2020.07.007>.

This package implements a general framework for utilizing R to harness the power of NVIDIA GPU's. The "gmatrix" and "gvector" classes allow for easy management of the separate device and host memory spaces. Numerous numerical operations are implemented for these objects on the GPU. These operations include matrix multiplication, addition, subtraction, the kronecker product, the outer product, comparison operators, logical operators, trigonometric functions, indexing, sorting, random number generation and many more.
Nvidia's CUDA toolkit (>= release 4.0) and Nvidia
GPU with compute capibility >=2.0

A general framework for utilizing R to harness the power of NVIDIA GPU's. The "gmatrix" and "gvector" classes allow for easy management of the separate device and host memory spaces. Numerous numerical operations are implemented for these objects on the GPU. These operations include matrix multiplication, addition, subtraction, the kronecker product, the outer product, comparison operators, logical operators, trigonometric functions, indexing, sorting, random number generation and many more.
Nvidia's CUDA toolkit (>= release 5.0) and Nvidia
GPU with compute capibility >=2.0