Title: Ensemble Empirical Mode Decomposition (EEMD) and Its Complete
Authors@R: c(person(given = "Jouni", family = "Helske", role = c("aut","cre"),
comment = "R interface",email = "jouni.helske@jyu.fi"), person(given =
"Perttu", family = "Luukko", role = "aut", comment = "Original libeemd C
Description: An R interface for C library libeemd for performing the ensemble
empirical mode decomposition (EEMD), its complete variant (CEEMDAN) or the
regular empirical mode decomposition (EMD).
Author: Jouni Helske [aut, cre] (R interface),
Perttu Luukko [aut] (Original libeemd C library)

comment = "R interface",email = "jouni.helske@iki.fi"), person(given =

comment = c("R interface", ORCID = "0000-0001-7130-793X"), email = "jouni.helske@iki.fi"),
person(given = "Perttu", family = "Luukko", role = "aut", comment = c("Original libeemd C
Description: An R interface for libeemd (Luukko, Helske, Räsänen, 2016) <doi:10.1007/s00180-015-0603-9>,
a C library of highly efficient parallelizable functions  for performing the ensemble empirical mode decomposition (EEMD),
its complete variant (CEEMDAN), the regular empirical mode decomposition (EMD), and bivariate EMD (BEMD).
Due to the possible portability issues CRAN version no longer supports OpenMP, you can install OpenMP-supported version
Author: Jouni Helske [aut, cre] (R interface,
Perttu Luukko [aut] (Original libeemd C library,

Description: A series of functions that allow users
to access the LinkedIn API to get information about connections,
search for people and jobs, share updates with their network,
and create group discussions.  For more information about using
the API please visit https://developer.linkedin.com/.
Depends: R (>= 2.12.0), httr, XML, httpuv

to access the 'LinkedIn' API to get information about connections,
the API please visit <https://developer.linkedin.com/>.

Title: Iterative Solvers for (Sparse) Linear System of Equations
Description: 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 - or, Krylov subspace methods
are also provided; Conjugate Gradient, Conjugate Gradient Squared, Biconjugate Gradient,
and Biconjugate Gradient Stabilized methods. 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)
Suggests: knitr, rmarkdown, microbenchmark, optR, pcg