productivity. See <https://eartharxiv.org/repository/view/2294/> for a description of the methods used in

Package: LPGraph
Type: Package
Title: Nonparametric Smoothing of Laplacian Graph Spectra
Version: 1.0
Date: 2018-03-27
Author: Subhadeep Mukhopadhyay, Kaijun Wang
Maintainer: Kaijun Wang <kaijun.wang@temple.edu>
Description: A nonparametric method to approximate Laplacian graph spectra of a network with
ordered vertices. This provides a computationally efficient algorithm for obtaining an
accurate and smooth estimate of the graph Laplacian basis. The approximation results can
then be used for tasks like change point detection, k-sample testing, and so on. The
primary reference is Mukhopadhyay, S. and Wang, K. (2018, Technical Report).
Depends: R (>= 2.10), stats, SDMTools, car
License: GPL-2
NeedsCompilation: no
Packaged: 2018-04-08 18:07:57 UTC; AquinasUnit
Repository: CRAN
Date/Publication: 2018-04-09 14:46:44 UTC

Package: LPGraph
Type: Package
Title: Nonparametric Smoothing of Laplacian Graph Spectra
Version: 2.0
Date: 2018-05-11
Author: Subhadeep Mukhopadhyay, Kaijun Wang
Maintainer: Kaijun Wang <kaijun.wang@temple.edu>
Description: A nonparametric method to approximate Laplacian graph spectra of a network with
ordered vertices. This provides a computationally efficient algorithm for obtaining an
accurate and smooth estimate of the graph Laplacian basis. The approximation results can
then be used for tasks like change point detection, k-sample testing, and so on. The
primary reference is Mukhopadhyay, S. and Wang, K. (2018, Technical Report).
Depends: R (>= 2.10), stats, SDMTools, car, PMA
License: GPL-2
NeedsCompilation: no
Packaged: 2018-05-11 14:03:38 UTC; AquinasUnit
Repository: CRAN
Date/Publication: 2018-05-13 03:04:48 UTC

Packaged: 2020-01-30 05:34:33 UTC; AquinasUnit

Title: LP Nonparametric High Dimension K-Sample Comparison
Description: LP nonparametric high-dimensional K-sample comparison method that includes (i) confirmatory test; (ii) exploratory results and (iii) options to output a data-driven LP-transformed matrix for classification. The primary reference is Mukhopadhyay, S. and Wang, K. (2017, Technical Report).
Depends: R (>= 2.10), apcluster, igraph, mclust
Packaged: 2017-11-17 18:06:50 UTC; AquinasUnit