knitr, rmarkdown, viridisLite (>= 0.4.0)

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).
R (>= 2.15.0), seqinr, pcaMethods, Matrix,
ROCR,Hmisc,KernSmooth, methods, seqLogo

R (>= 2.11.0), seqinr, pcaMethods, Matrix,
ROCR,Hmisc,KernSmooth, methods, seqLogo

Multiscale Clustering of Geometrical Network
Co-Expression Network Analysis by adopting network embedding technique.

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>.

Data sets from Mixed-effects Models in S
Data sets and sample analyses from Pinheiro and Bates,
"Mixed-effects Models in S and S-PLUS" (Springer, 2000).

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

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

Data Sets from Mixed-Effects Models in S

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".
J. L. Bentley (1980) <doi:10.1145/358841.358850>.

Creation of Empirical Density Functions Based on Multivariate
Data
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".
J. L. Bentley (1980) <doi:10.1145/358841.358850>.
Furthermore a simple kernel density estimation method is made available, as well as an expansion of Bentleys
method, which offers a kernel approach for the grid method.