A Super K-Nearest Neighbor (SKNN) Classification Algorithm
c(person("Yi", "Ya", role=c("aut","cre"),email="Yi.YA_yaya@hotmail.com"),
person("Yarong", "Yang", role="aut"),
person("Nader", "Ebrahimi", role="aut"),
person("Jacob", "Zhang", role="aut"))
It's a Super K-Nearest Neighbor classification method with using kernel density to describe weight of the distance between a training observation and the testing sample.
AllClasses.R KDEy.R SKNN.R Dist.R PCAy.R UKDE.R AKDE.R
Yi Ya [aut, cre],
Yarong Yang [aut],
Nader Ebrahimi [aut],
Jacob Zhang [aut]

c(person("Yi", "Ya", role=c("aut","cre"),email="Yi.YA_yaya@hotmail.com"),
person("Yarong", "Yang", role="aut"),
person("Nader", "Ebrahimi", role="aut"),
person("Yoram", "Rubin", role="aut"),
person("Jacob", "Zhang", role="aut"))

c(person("Yi", "Ya", role=c("aut","cre"),email="Yi.YA_yaya@hotmail.com"),
person("Nader", "Ebrahimi", role="aut"),
person("Yoram", "Rubin", role="aut"),
person("Jacob", "Zhang", role="aut"))

Statistical Learning for Big Dependent Data
c(person("Angela", "Caro", email = "", role = c("aut")),
person("Antonio", "Elias", email = "antonioefz91@gmail.com", role = c("aut", "cre")),
person("Daniel", "Peña", email = "", role = c("aut")),
person("Ruey S.", "Tsay", email = "", role = c("aut")))
Programs for analyzing large-scale time series data. They include functions for automatic specification and estimation of univariate time series, for clustering time series, for multivariate outlier detections, for quantile plotting of many time series, for dynamic factor models and for creating input data for deep learning programs. Examples of using the package can be found in the Wiley book 'Statistical Learning with Big Dependent Data' by Daniel Peña and Ruey S. Tsay (2021). ISBN 9781119417385.
stats, glmnet, corpcor, forecast, gsarima, cluster, fGarch,
imputeTS, methods, MASS, MTS, TSclust, tsoutliers, Matrix,
matrixcalc, rnn
Angela Caro [aut],
Antonio Elias [aut, cre],
Daniel Peña [aut],
Ruey S. Tsay [aut]

Antonio Solanas, Rumen Manolov, and Patrick Onghena
Estimates the slope and level change present in data after
removing phase A trend. Represents graphically the original and
the detrended data.