Author: James Honaker \email{zelig-zee@@iq.harvard.edu} Matt Owen, Kosuke Imai, Olivia Lau and Gary King
Description: Add-on package for Zelig. Enables the use of a variety of logit

Description: Add-on package for Zelig 5. Enables the use of a variety of logit
Imports: methods, Zelig (>= 5.0-9), MASS, VGAM, jsonlite, dplyr

Imports: methods, Zelig (>= 5.0-10), MASS, VGAM, jsonlite, dplyr

Imports: methods, Zelig (>= 5.0-14), MASS, VGAM, jsonlite, dplyr
Packaged: 2017-02-02 04:13:29 UTC; christophergandrud

Imports: dplyr, jsonlite, MASS, methods, VGAM, Zelig (>= 5.0-14),
Packaged: 2017-02-06 12:19:36 UTC; christophergandrud

Imports: dplyr, jsonlite, MASS, methods, VGAM, Zelig (>= 5.0-16),
Packaged: 2017-03-01 23:33:53 UTC; christophergandrud

Imports: dplyr, Formula, jsonlite, MASS, methods, VGAM, Zelig (>

Imports: dplyr, Formula, jsonlite, MASS, methods, VGAM, Zelig (>=

Title: Zelig Ecological Inference Models
Description: Add-on package for Zelig 5. Enables the use of a variety of
Imports: methods, Zelig (>= 5.0-11), MASS, jsonlite, dplyr, eiPack, ei,

person("James", "Honaker", role = "aut")
Imports: dplyr, ei, Formula, jsonlite, MASS, MCMCpack, methods, Zelig
Packaged: 2017-05-22 17:13:45 UTC; christophergandrud

Title: Genereal Additive Models for Zelig
Author: Matthew Owen, Skyler Cranmer, Olivia Lau, Kosuke Imai and Gary
Description: General Additive Models extend GLM's to support the
application of smoothing functions to the predictor terms.

Title: General Additive Models for Zelig
Author: Matthew Owen, Skyler Cranmer, Olivia Lau, Kosuke Imai and Gary King
Description: General Additive Models extend GLM's to support the application of
smoothing functions to the predictor terms.

Author: Matthew Owen, Ferdinand Alimadhi, Delia Bailey
Description: Add-on package for Zelig. Enables the use of multilevel

Description: Add-on package for Zelig. Enables the use of multilevel (mixed-effect) models.

Title: Analyze High-Dimensional High-Throughput Dataset and Quality
person(given = "Yajing", family = "Hao", email = "yahao@health.ucsd.edu", role  = c("aut"), comment = c(ORCID = "0000-0003-1384-4176")),
person(given = "Shuyang", family = "Zhang", email = "s4zhang@ucsd.edu", role  = "ctb", comment=c(ORCID="0000-0002-8428-1828")),
person(given = "Junhui", family = "Li", email = "ljh.biostat@gmail.com", role = "cre"),
person(given = "Guofeng", family = "Zhao", email = "gzhao@health.ucsd.edu", role = "ctb"),
person(given = "Xiang-Dong", family = "Fu", email = "xdfu@health.ucsd.edu", role = c("cph","fnd"), comment = c(ORCID = "0000-0001-5499-8732"))
Description: The advent of genomic technologies has enabled the generation of two-dimensional or even multi-dimensional high-throughput data, e.g., monitoring multiple changes in gene expression in genome-wide siRNA screens across many different cell types (E Robert McDonald 3rd (2017) <doi: 10.1016/j.cell.2017.07.005> and Tsherniak A (2017) <doi: 10.1016/j.cell.2017.06.010>) or single cell transcriptomics under different experimental conditions. We found that simple computational methods based on a single statistical criterion is no longer adequate for analyzing such multi-dimensional data. We herein introduce 'ZetaSuite', a statistical package initially designed to score hits from two-dimensional RNAi screens.We also illustrate a unique utility of 'ZetaSuite' in analyzing single cell transcriptomics to differentiate rare cells from damaged ones (Vento-Tormo R (2018) <doi: 10.1038/s41586-018-0698-6>). In 'ZetaSuite', we  have the following steps: QC of input datasets, normalization using Z-transformation, Zeta score calculation and hits selection based on defined Screen Strength.
Imports: RColorBrewer, Rtsne, dplyr, e1071, ggplot2, reshape2, scater,