Imports: whisker, jsonlite, yaml, utils, httr
Suggests: knitr, rmarkdown, dplyr, shiny

Authors@R: c(person("Edwin", "de Jonge", email="edwindjonge@gmail.com", role = c("aut","cre")))

Authors@R: c(person("Edwin", "de Jonge", email="edwindjonge@gmail.com", role = c("aut","cre")),
Netherlands (<https://www.cbs.nl>) can be browsed and downloaded. The client uses

Suggests: knitr, rmarkdown, dplyr, shiny, testthat (>= 2.1.0)

Author: Marcin Studnicki and Konrad Debski
Description: ccChooser can be used to developing and evaluation of core
collections for germplasm collections (entire collection). This
package used to develop a core collection for biological
resources like genbanks. A core collection is defined as a
sample of accessions that represent, with the lowest possible
level of redundancy, the genetic diversity (the richness of
gene or genotype categories) of the entire collection. The
establishing a core collection that represents genetic
diversity of the entire collection with minimum loss of its
original diversity and minimum redundancies is an important
problem for gene-banks curators and crop breeders. ccChooser
establish core collection base on phenotypic data (agronomic,

Title: Removes the Cell-Cycle Effect from Single-Cell RNA-Sequencing
Description: Implements a method for identifying and removing
the cell-cycle effect from scRNA-Seq data. The description of the
method is in Barron M. and Li J. (2016) <doi:10.1038/srep33892>. Identifying and removing
the cell-cycle effect from single-cell RNA-Sequencing data. Submitted.
Different from previous methods, ccRemover implements a mechanism that
formally tests whether a component is cell-cycle related or not, and thus
while it often thoroughly removes the cell-cycle effect, it preserves
other features/signals of interest in the data.
Packaged: 2017-05-18 05:11:03 UTC; Administrator

Packaged: 2017-07-25 19:33:20 UTC; Administrator

Packaged: 2017-08-04 15:36:29 UTC; Administrator

Packaged: 2017-08-17 15:33:13 UTC; Administrator

Authors@R: c(person("Koki", "Tsuyuzaki", role = c("aut", "cre"),
Imports: methods, fields, MASS, igraph, rTensor
Description: CUR/CX decomposition factorizes a matrix into two factor matrices and Multidimensional CX Decomposition factorizes a tensor into a core tensor and some factor matrices. See the reference section of GitHub README.md <https://github.com/rikenbit/ccTensor>, for details of the methods.