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

A suite of elliptic and related functions including
Weierstrass and Jacobi forms.  Also includes various tools for
manipulating and visualizing complex functions.

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

Weierstrass and Jacobi Elliptic Functions

c(person(given = "Sladana",
family = "Babic",
role = c("aut"),
email = "sladana.babic@ugent.be",
comment = c(ORCID = "https://orcid.org/0000-0001-8076-4665")),
person(given = "Marko",
family = "Palangetic",
role = c("aut", "cre"),
email = "marko.palangetic@ugent.be",
comment = c(ORCID = "https://orcid.org/0000-0002-6366-0634")),
person(given = "Christophe",
family = "Ley",
role = c("aut"),
email = "christophe.ley@ugent.be",
comment = c(ORCID = "https://orcid.org/0000-0002-2290-8437")))
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>.

c(person(given = "Slađana",
family = "Babić",
role = c("aut"),
email = "sladana.babic@ugent.be",
comment = c(ORCID = "https://orcid.org/0000-0001-8076-4665")),
person(given = "Marko",
family = "Palangetić",
role = c("aut", "cre"),
email = "marko.palangetic@ugent.be",
comment = c(ORCID = "https://orcid.org/0000-0002-6366-0634")),
person(given = "Christophe",
family = "Ley",
role = c("aut"),
email = "christophe.ley@ugent.be",
comment = c(ORCID = "https://orcid.org/0000-0002-2290-8437")))
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. (2020) <arXiv:2011.12560>.

Implementation of ELM (Extreme Learning Machine ) algorithm for
SLFN ( Single Hidden Layer Feedforward Neural Networks )
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.