Matt Wand <Matt.Wand@uts.edu.au> R port by Brian Ripley
<ripley@stats.ox.ac.uk>.

c(person("Matt", "Wand", role = "aut", email =
"Matt.Wand@uts.edu.au"), person("Brian", "Ripley", role =
c("trl", "cre", "ctb"), email = "ripley@stats.ox.ac.uk"))
Matt Wand <Matt.Wand@uts.edu.au>
R port and updates by Brian Ripley <ripley@stats.ox.ac.uk>.

Matt Wand <Matt.Wand@uts.edu.au> R port and updates by Brian
Ripley <ripley@stats.ox.ac.uk>.

Angelo Mazza, Antonio Punzo, Brian McGuire
This package fits nonparametric item and option
characteristic curves using kernel smoothing. It allows for
optimal selection of the smoothing bandwidth using
cross-validation and a variety of exploratory plotting tools.

This package fits nonparametric item and option characteristic curves using kernel smoothing. It allows for optimal selection of the smoothing bandwidth using cross-validation and a variety of exploratory plotting tools.

Fits nonparametric item and option characteristic curves using kernel smoothing. It allows for optimal selection of the smoothing bandwidth using cross-validation and a variety of exploratory plotting tools. The kernel smoothing is based on methods described in Silverman, B.W. (1986). Density Estimation for Statistics and Data Analysis. Chapman & Hall, London.

c(person(
given = "Christoph L.",
family = "Koesner",
role = c("aut", "cre"),
email = "christoph@koesner.at",
comment = c(ORCID = "0000-0002-2061-8022")),
person(
given = "Klaus",
family = "Nordhausen",
role = "aut",
comment = c(ORCID = "0000-0002-3758-8501")))
The kernel independent component analysis (kernel ICA) method introduced by Bach and Jordan (2003) <doi:10.1162/153244303768966085>. The incomplete Cholesky decomposition used in kernel ICA is provided as separate function.
inline, methods, ManifoldOptim, JADE, ICtest
Christoph L. Koesner [aut, cre]
(<https://orcid.org/0000-0002-2061-8022>),
Klaus Nordhausen [aut] (<https://orcid.org/0000-0002-3758-8501>)

Extends the simple k-nearest neighbors algorithm by incorporating numerous kernel functions and a variety of distance metrics. The package takes advantage of 'RcppArmadillo' to speed up computationally intensive functions.