Data Generation with Binary and Continuous Non-normal Components
Generation of multiple binary and continuous non-normal variables simultaneously
given the marginal characteristics and association structure based on the methodology
proposed by Demirtas et al. (2012).

Data Generation with Binary and Continuous Non-Normal Components

Generation of multiple binary and continuous non-normal variables simultaneously
given the marginal characteristics and association structure based on the methodology
proposed by Demirtas et al. (2012) <DOI:10.1002/sim.5362>.

Simultaneous generation of multivariate binary and normal
variates.
Generating multiple binary and normal variables simultaneously given marginal characteristics and association structure based on the methodology proposed by Demirtas and Doganay (2012).

Generating multiple binary and normal variables
simultaneously given marginal characteristics and association
structure based on the methodology proposed by Demirtas and
Doganay (2012).

Simultaneous Generation of Multivariate Binary and Normal
Variates

Generating multiple binary and normal variables simultaneously given marginal characteristics and association structure based on the methodology proposed by Demirtas and Doganay (2012) <DOI:10.1080/10543406.2010.521874>.

Concurrent Generation of Binary, Ordinal and Continuous Data
Generation of samples from a mix of binary, ordinal and continuous random variables with a pre-specified correlation matrix and marginal distributions.

Hakan Demirtas, Yue Wang, Rawan Allozi, Ran Gao
Generation of samples from a mix of binary, ordinal and continuous random variables with a pre-specified correlation matrix and marginal distributions. The details of the method are explained in Demirtas et al. (2012) <DOI:10.1002/sim.5362>.

Analyzing Replicated ChIP Sequencing Data Using Quasi-Likelihood
c(
person("Emily", "Goren", role=c("aut", "cre"), email="emily.goren@gmail.com"),
person("Steve", "Lund", role="aut", comment="The author of the QuasiSeq package, from which all functions were modified to produce this package."),
person("Long", "Qu", role="aut", comment="The author of the QuasiSeq package, from which all functions were modified to produce this package."),
person("Ian", "Marschner", role="aut", comment="The author of glm2::glm.fit2, which was modified slightly leading to glm.fit3 in this package."),
person("Daniel", "Gerhard", role="aut", comment="The author of mcprofile::orglm.fit, which was modified slightly and used under the same name in this package."),
person("R Core Team", role="aut", comment="The author of stats::glm.fit, which was modified slightly leading to glm.fit3 in this package.")
)
edgeR, mgcv, pracma, quadprog, Rsamtools, GenomicAlignments,
GenomicRanges, IRanges, csaw(>= 1.12.0), SummarizedExperiment,
BiocGenerics, S4Vectors
Identify peaks in ChIP-seq data with biological replicates using a one-sided quasi-likelihood ratio test in quasi-Poisson or quasi-negative binomial models.
Emily Goren [aut, cre],
Steve Lund [aut] (The author of the QuasiSeq package, from which all
functions were modified to produce this package.),
Long Qu [aut] (The author of the QuasiSeq package, from which all
functions were modified to produce this package.),
Ian Marschner [aut] (The author of glm2::glm.fit2, which was modified
slightly leading to glm.fit3 in this package.),
Daniel Gerhard [aut] (The author of mcprofile::orglm.fit, which was
modified slightly and used under the same name in this package.),
R Core Team [aut] (The author of stats::glm.fit, which was modified
slightly leading to glm.fit3 in this package.)