c(
person("Thomas Lin", "Pedersen", email = "thomasp85@gmail.com", role = c("aut", "cre")),
person("Sean", "Hughes", email = "", role = c("aut")),
person("Xiaojie", "Qiu", email = "xqiu@uw.edu", role = c("aut")))
An improved implementation (based on k-nearest neighbors) of the
density peak clustering algorithm, originally described by Alex Rodriguez
and Alessandro Laio (Science, 2014 vol. 344) <DOI: 10.1126/science.1242072>.
It can handle large datasets (> 100, 000 samples) very efficiently. It was
initially implemented by Thomas Lin Pedersen, with inputs from Sean Hughes
and later improved by Xiaojie Qiu to handle large datasets with kNNs.
Rcpp, FNN, Rtsne, ggplot2, ggrepel, grDevices, gridExtra,
RColorBrewer
Thomas Lin Pedersen [aut, cre],
Sean Hughes [aut],
Xiaojie Qiu [aut]

An improved implementation (based on k-nearest neighbors) of the
density peak clustering algorithm, originally described by Alex Rodriguez
and Alessandro Laio (Science, 2014 vol. 344). It can handle large datasets
(> 100, 000 samples) very efficiently. It was initially implemented by
Thomas Lin Pedersen, with inputs from Sean Hughes and later improved by
Xiaojie Qiu to handle large datasets with kNNs.

Density ratio estimation.
The estimated density ratio function can be used in many applications such as
the inlier-based outlier detection, covariate shift adaptation and etc.
person("Koji", "Makiyama", , "hoxo.smile@gmail.com", c("aut", "cre"))
graphics, knitr, mvtnorm, rmarkdown, stats

Density ratio estimation.
The estimated density ratio function can be used in many applications such as
the inlier-based outlier detection, covariate shift adaptation and etc.
The implemented methods are uLSIF (Hido et al. (2011) <doi:10.1007/s10115-010-0283-2>)
and KLIEP (Sugiyama et al. (2007) <doi:10.1007/s10463-008-0197-x>).
graphics, knitr, mvtnorm, rmarkdown, stats, testthat

Density ratio estimation.
The estimated density ratio function can be used in many applications such as
anomaly detection, change-point detection, covariate shift adaptation.
The implemented methods are uLSIF (Hido et al. (2011) <doi:10.1007/s10115-010-0283-2>),
RuLSIF (Yamada et al. (2011) <doi:10.1162/NECO_a_00442>),
and KLIEP (Sugiyama et al. (2007) <doi:10.1007/s10463-008-0197-x>).