Multi-Scale Motions Separation with Kolmogorov-Zurbenko
Periodogram Signals
c(person("Ming", "Luo", email = "ml226662@gmail.com", role = c("aut", "cre")),
person("Igor", "Zurbenko", email = "IZurbenko@albany.edu", role = "aut"))
Separation of wave motions in different scales and directions based on
Kolmogorov-Zurbenko 2D periodograms and directional periodograms.
Ming Luo [aut, cre],
Igor Zurbenko [aut]

Ming Luo <ml226662@gmail.com> and Igor Zurbenko <IZurbenko@albany.edu>

Separation of wave motions in different scales and directions based on
Kolmogorov-Zurbenko Periodograms and Kolmogorov-Zurbenko Fourier Transform.

Kolmogorov-Zurbenko Fourier transform and application
Wei Yang <peterwyang@gmail.com> and Igor Zurbenko
<IZurbenko@albany.edu>
A colletion of functions to implement Kolmogorov-Zurbenko
Fourier transform based periodograms and smoothing methods

Kolmogorov-Zurbenko Fourier Transform and Applications
Wei Yang <peterwyang@gmail.com> and Igor Zurbenko
<igorg.zurbenko@gmail.com>

Derek Cyr <dc896148@albany.edu> and Igor Zurbenko <igorg.zurbenko@gmail.com>.
The Kolmogorov-Zurbenko Spline function utilizes the moving average to construct
a piece-wise estimator of the underlying signal of the given input data.

A collection of functions utilizng splines to smooth a noisy data set in order
to estimate its underlying signal.

Kolmogorov-Zurbenko Spline Smoothing and Applications
Derek Cyr <cyr.derek@gmail.com> and Igor Zurbenko <igorg.zurbenko@gmail.com>.
A collection of functions utilizng splines to construct a smooth estimate
of a signal buried in noise.

Kolmogorov-Zurbenko Spatial Smoothing and Applications
A spatial smoothing algorithm based on convolutions of finite rectangular kernels that provides sharp resolution in the presence of high levels of noise.

Sparse Generalized Linear Model with L0 Approximation for
Feature Selection
Wenchuan Guo, Zhenqiu Liu, Fengzhu Sun, Dermot P McGovern, Sandra
Orsulic
An efficient procedure for feature selection for generalized linear models with L0 penalty, including linear, logistic, Poisson, gamma, inverse Gaussian regression. Adaptive ridge algorithms are used to fit the models.