Mixed Poisson Regression for Overdispersed Count Data
c(person(given = "Alexandre B.",
family = "Simas",
role = c("aut", "cre"),
email = "alexandre.impa@gmail.com",
comment = c(ORCID = "0000-0003-2562-2829")),
person(given = "Wagner",
family = "Barreto-Souza",
role = "aut",
email = "wagnerbs85@gmail.com",
comment = c(ORCID = "0000-0003-0831-7881")))
Fits mixed Poisson regression models (Poisson-Inverse Gaussian or Negative-Binomial) on data sets with response variables being count data. The models can have varying precision parameter, where a linear regression structure (through a link function) is assumed to hold on the precision parameter. The Expectation-Maximization algorithm for both these models (Poisson Inverse Gaussian and Negative Binomial) is an important contribution of this package. Another important feature of this package is the set of functions to perform global and local influence analysis. See Barreto-Souza and Simas (2016) <doi:10.1007/s11222-015-9601-6> for further details.
pbapply, Formula, Rfast, dplyr, gamlss.dist, generics,
ggplot2, gridExtra, lmtest, magrittr, statmod, tibble, rlang,
ggrepel, gamlss
rmarkdown, knitr, devtools, bbreg, testthat, covr, tidyr,
ggfortify, broom
Alexandre B. Simas [aut, cre] (<https://orcid.org/0000-0003-2562-2829>),
Wagner Barreto-Souza [aut] (<https://orcid.org/0000-0003-0831-7881>)

Functions to fit mixtures of regressions.
Fits mixtures of one-variable regressions (which has
been described as doing ANCOVA when you don't know the levels).

Fits mixtures of (possibly multivariate) regressions
(which has been described as doing ANCOVA when you don't
know the levels).

Fits mixtures of (possibly multivariate) regressions
(which has been described as doing ANCOVA when you don't know
the levels).

Functions to Fit Mixtures of Regressions

Fits mixtures of (possibly multivariate) regressions
(which has been described as doing ANCOVA when you don't
know the levels). Turner (2000) <doi:10.1111/1467-9876.00198>.

Forensic Genetics DNA Mixture Separation
Separates DNA mixtures using a statistical model within a
greedy algorithm.
R (>= 2.0.0), tcltk, tcltk2, MASS, RODBC