Depends: R (>= 2.5.0), emulator, calibrator, MASS

Title: Weierstrass and Jacobi Elliptic Functions

Suggests: emulator, calibrator (>= 1.2-8)

Description: Given the omnipresence of the assumption of elliptical symmetry, it is essential to be able to test whether that assumption actually holds true or not for the data
at hand. This package provides several statistical tests for elliptical symmetry that are described in Babic et al. (2021) <arXiv:2011.12560v2>.
Packaged: 2021-04-07 12:01:42 UTC; markopalangetic
Author: Sladana Babic [aut] (<https://orcid.org/0000-0001-8076-4665>),
Marko Palangetic [aut, cre] (<https://orcid.org/0000-0002-6366-0634>),

at hand. This package provides several statistical tests for elliptical symmetry that are described in Babic et al. (2020) <arXiv:2011.12560>.
Packaged: 2020-11-26 22:00:03 UTC; markopalangetic
Author: Slađana Babić [aut] (<https://orcid.org/0000-0001-8076-4665>),
Marko Palangetić [aut, cre] (<https://orcid.org/0000-0002-6366-0634>),

Title: Implementation of ELM (Extreme Learning Machine ) algorithm for
SLFN ( Single Hidden Layer Feedforward Neural Networks )
Description: Training and predict functions for SLFN ( Single
Hidden-layer Feedforward Neural Networks ) using the ELM
algorithm. ELM algorithm differs from the traditional
gradient-based algorithms for very short training times ( it
doesn't need any iterative tuning, this makes learning time
very fast ) and there is no need to set any other parameters
like learning rate, momentum, epochs, etc.

Title: The Extreme Learning Machine Algorithm
Authors@R: c( person("Lampros", "Mouselimis", email = "mouselimislampros@gmail.com", role = c("aut", "cre")), person("Alberto", "Gosso", email = "gosso.alberto@gmail.com", role = "aut") )
Description: Training and predict functions for Single Hidden-layer Feedforward Neural Networks (SLFN) using the Extreme Learning Machine (ELM) algorithm. The ELM algorithm differs from the traditional gradient-based algorithms for very short training times (it doesn't need any iterative tuning, this makes learning time very fast) and there is no need to set any other parameters like learning rate, momentum, epochs, etc. This is a reimplementation of the 'elmNN' package using 'RcppArmadillo' after the 'elmNN' package was archived. For more information, see "Extreme learning machine: Theory and applications" by Guang-Bin Huang, Qin-Yu Zhu, Chee-Kheong Siew (2006), Elsevier B.V, <doi:10.1016/j.neucom.2005.12.126>.