person(c("Ahmet", "Ergun"), "Karaagaoglu", role = c("ths")))
Description: We proposed a package for the classification task which uses Negative Binomial distribution within Linear Discriminant Analysis (NBLDA). It is an extension of the 'PoiClaClu' package to Negative Binomial distribution. The classification algorithms are based on the papers Dong et al. (2016, ISSN: 1471-2105) and Witten, DM (2011, ISSN: 1932-6157) for NBLDA and PLDA, respectively. Although PLDA is a sparse algorithm and can be used for variable selection, the algorithm proposed by Dong et al. is not sparse. Therefore, it uses all variables in the classifier. Here, we extend Dong et al.'s algorithm to the sparse case by shrinking overdispersion towards 0 (Yu et al., 2013, ISSN: 1367-4803) and offset parameter towards 1 (as proposed by Witten DM, 2011). We support only the classification task with this version.

Title: The NBP Negative Binomial Model for Assessing Differential Gene
Author: Yanming Di <diy@stat.oregonstate.edu>, Daniel W Schafer
<schafer@stat.oregonstate.edu>, with contributions from Jason S
Cumbie <cumbiej@onid.orst.edu> and Jeff H Chang
Description: Fit NBP model to RNA-Seq count data and identify genes
differentially expressing in two groups.

Title: Negative Binomial Models for RNA-Sequencing Data
Description: Negative Binomial (NB) models for two-group comparisons
and regression inferences from RNA-Sequencing Data.

<schafer@stat.oregonstate.edu>, with contributions from Jason S Cumbie
<cumbiej@onid.orst.edu> and Jeff H Chang <changj@cgrb.oregonstate.edu>.
Description: Negative Binomial (NB) models for two-group comparisons and
regression inferences from RNA-Sequencing Data.

Title: Network Based Statistics in R for Mixed Effects Models
person("Zeus", "Gracia-Tabuenca", email = "zgtabuenca@comunidad.unam.mx", role =  c("aut", "cre")),
person("Sarael", "Alcauter", email = "alcauter@inb.unam.mx", role = "aut"))
Author: Zeus Gracia-Tabuenca [aut, cre],
Description: An implementation of network-based statistics in R for mixed effects models.
Theoretical background for Network Based Statistics can be found in Zalesky et al. (2010)
<doi:10.1016/j.neuroimage.2010.06.041>. For Mixed Effects Models check the
Suggests: graphics, knitr, lattice, rmarkdown
Packaged: 2020-03-24 19:24:36 UTC; zgtabuenca