Author: Derek Cyr <dc896148@albany.edu> and Igor Zurbenko <igorg.zurbenko@gmail.com>.
Description: The Kolmogorov-Zurbenko Spline function utilizes the moving average to construct
a piece-wise estimator of the underlying signal of the given input data.
Packaged: Wed Jun 27 15:28:28 2007; Owner

Description: A collection of functions utilizng splines to smooth a noisy data set in order
Packaged: Sun Jul  1 15:30:14 2007; Owner

Title: Kolmogorov-Zurbenko Spline Smoothing and Applications
Author: Derek Cyr <cyr.derek@gmail.com> and Igor Zurbenko <igorg.zurbenko@gmail.com>.
Depends: R (>= 2.6.0), graphics, lattice, stats
Description: A collection of functions utilizng splines to construct a smooth estimate
Packaged: Fri Nov  2 23:36:30 2007; Owner

Depends: R (>= 2.6.2), graphics, lattice, stats
Packaged: Thu Feb 28 23:37:06 2008; Owner

Title: Kolmogorov-Zurbenko Spatial Smoothing and Applications
Depends: R (>= 2.8.0), graphics, lattice, stats
Description: A spatial smoothing algorithm based on convolutions of finite rectangular kernels that provides sharp resolution in the presence of high levels of noise.
Packaged: Tue Oct 28 13:41:02 2008; Owner

Title: Sparse Generalized Linear Model with L0 Approximation for
Author: Wenchuan Guo, Zhenqiu Liu, Fengzhu Sun, Dermot P McGovern, Sandra
Description: 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.

Author: Wenchuan Guo, Shujie Ma, Zhenqiu Liu

Title: L1-Ball Prior for Sparse Regression
Description: Provides function for the l1-ball prior on high-dimensional regression. The main function, l1ball(), yields posterior samples for linear regression, as introduced by Xu and Duan (2020) <arXiv:2006.01340>.

Title: Deconvolution for LINCS L1000 Data
Description: LINCS L1000 is a high-througphput technology that allows the gene expression measurement in a large number of assays. However, to fit the measurements of ~1000 genes in the ~500 color channels of LINCS L1000, every two landmark genes are designed to share a single channel. Thus, a deconvolution step is required to infer the expression values of each gene. Any errors in this step can be propagated adversely to the downstream analyses. We present a LINCS L1000 data peak calling R package l1kdeconv based on a new outlier detection method and an aggregate Gaussian mixture model (AGMM). Upon the remove of outliers and the borrowing information among similar samples, l1kdeconv shows more stable and better performance than methods commonly used in LINCS L1000 data deconvolution.