Solving a system of linear equations is one of the most fundamental
computational problems for many fields of mathematical studies, such as
regression problems from statistics or numerical partial differential equations.
We provide basic stationary iterative solvers such as Jacobi, Gauss-Seidel,
Successive Over-Relaxation and SSOR methods. Nonstationary, also known as
Krylov subspace methods are also provided. Sparse matrix computation is also supported
in that solving large and sparse linear systems can be manageable using 'Matrix' package
along with 'RcppArmadillo'. For a more detailed description, see a book by Saad (2003)
<doi:10.1137/1.9780898718003>.

person("Kisung", "You", email = "kyou@nd.edu", role = c("aut", "cre"),comment=c(ORCID="0000-0002-8584-459X"))

person("Kisung", "You", email = "kyou@nd.edu", role = c("aut", "cre"), comment=c(ORCID="0000-0002-8584-459X"))

person("Kisung", "You", email = "kyoustat@gmail.com", role = c("aut", "cre"), comment=c(ORCID="0000-0002-8584-459X"))
Rcpp (>= 0.12.4), Matrix, Rdpack, stats, utils

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R parallel implementation of Local Outlier Factor(LOF)
Yingsong Hu, Wayne Murray and Yin Shan with Strategic Data
Mining Team, Department of Human Services, Australian.
R parallel implementation of Local Outlier Factor(LOF)
which uses multiple cpus to significantly speed up the LOF
computation for large datasets. (Note: The overall performance
depends on the computers especially the number of the cores).
It also supports multiple k values to be calculated in
parallel, as well as various distance measures in addition to
the default Euclidean distance.

R Parallel Implementation of Local Outlier Factor(LOF)
Yingsong Hu, Wayne Murray and Yin Shan, Australia.
R parallel implementation of Local Outlier Factor(LOF) which uses multiple CPUs to significantly speed up the LOF computation for large datasets. (Note: The overall performance depends on the computers especially the number of the cores).It also supports multiple k values to be calculated in parallel, as well as various distance measures in addition to the default Euclidean distance.