Suggests: knitr, rmarkdown, viridisLite (>= 0.4.0)

Title: MEET: Motif Elements Estimation Toolkit
Description: MEET (Motif Elements Estimation Toolkit) is a R-package
that integrates a set of computational algorithms for the
detection of Transcription Factor Binding Sites (TFBS).
Depends: R (>= 2.15.0), seqinr, pcaMethods, Matrix,

Depends: R (>= 2.11.0), seqinr, pcaMethods, Matrix,

Title: Multiscale Clustering of Geometrical Network
Description: Co-Expression Network Analysis by adopting network embedding technique.
Depends: R (>= 3.0.1),doParallel (>= 1.0.8),foreach (>= 1.4.2),igraph

Description: Co-Expression Network Analysis by adopting network embedding technique. Song W.-M., Zhang B. (2015) Multiscale Embedded Gene Co-expression Network Analysis. PLoS Comput Biol 11(11): e1004574. <doi: 10.1371/journal.pcbi.1004574>.

Title: Data sets from Mixed-effects Models in S
Description: Data sets and sample analyses from Pinheiro and Bates,
"Mixed-effects Models in S and S-PLUS" (Springer, 2000).
Packaged: Thu Feb 24 17:39:40 2005; bates

Packaged: Fri Apr  1 09:32:54 2005; bates

Packaged: Wed Jan  4 12:31:06 2006; bates

Packaged: Thu Jan 19 14:48:34 2006; bates

Packaged: Thu Sep 21 08:18:46 2006; bates

Packaged: Wed Jun 25 11:16:37 2008; bates

Packaged: Tue Feb 10 16:25:59 2009; bates

Author: Douglas Bates <bates@stat.wisc.edu>, Martin Maechler
<maechler@R-project.org> and Ben Bolker <bbolker@gmail.com>

Packaged: 2014-03-11 16:22:27 UTC; stevenwalker

Title: Data Sets from Mixed-Effects Models in S
Packaged: 2019-02-08 02:04:21 UTC; Steve_Walker

Title: Multivariate Empirical Density Function
Author: Martin Wiegand, Saralees Nadarajah
Description: Based on the input data an n-dimensional cube with sub cells of user specified side length is created.
The number of sample points which fall in each sub cube is counted, and with the cell volume and overall sample
size an empirical probability can be computed. A number of cubes of higher resolution can be superimposed. The
basic method stems from J.L. Bentley in "Multidimensional Divide and Conquer".