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BinaryEPPM: Mean and Scale-Factor Modeling of Under- And Over-Dispersed Binary Data

Under- and over-dispersed binary data are modeled using an extended Poisson process model (EPPM) appropriate for binary data. A feature of the model is that the under-dispersion relative to the binomial distribution only needs to be greater than zero, but the over-dispersion is restricted compared to other distributional models such as the beta and correlated binomials. Because of this, the examples focus on under-dispersed data and how, in combination with the beta or correlated distributions, flexible models can be fitted to data displaying both under- and over-dispersion. Using Generalized Linear Model (GLM) terminology, the functions utilize linear predictors for the probability of success and scale-factor with various link functions for p, and log link for scale-factor, to fit a variety of models relevant to areas such as bioassay. Details of the EPPM are in Faddy and Smith (2012) <doi:10.1002/bimj.201100214> and Smith and Faddy (2019) <doi:10.18637/jss.v090.i08>.

Version: 3.0
Depends: R (≥ 3.5.0)
Imports: Formula, expm, numDeriv, stats, lmtest, grDevices, graphics
Suggests: R.rsp
Published: 2024-06-04
DOI: 10.32614/CRAN.package.BinaryEPPM
Author: David M. Smith [aut, cre], Malcolm J. Faddy [aut]
Maintainer: David M. Smith <dmccsmith at verizon.net>
License: GPL-2
NeedsCompilation: no
Citation: BinaryEPPM citation info
CRAN checks: BinaryEPPM results

Documentation:

Reference manual: BinaryEPPM.pdf
Vignettes: Mean and Scale-Factor Modeling of Under- and Overdispersed Grouped Binary Data

Downloads:

Package source: BinaryEPPM_3.0.tar.gz
Windows binaries: r-devel: BinaryEPPM_3.0.zip, r-release: BinaryEPPM_3.0.zip, r-oldrel: BinaryEPPM_3.0.zip
macOS binaries: r-release (arm64): BinaryEPPM_3.0.tgz, r-oldrel (arm64): BinaryEPPM_3.0.tgz, r-release (x86_64): BinaryEPPM_3.0.tgz, r-oldrel (x86_64): BinaryEPPM_3.0.tgz
Old sources: BinaryEPPM archive

Linking:

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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.