Description: Quantitative RT-PCR data are analyzed using generalized linear mixed models based on lognormal-Poisson error distribution, fitted using MCMC. Control genes are not required but can be incorporated as Bayesian priors or, when template abundances correlate with conditions, as trackers of global effects (common to all genes). The package also implements a lognormal model for higher-abundance data and a "classic" model involving multi-gene normalization on a by-sample basis. Several plotting functions are included to extract and visualize results. The detailed tutorial is available here: http://bit.ly/1Nwo4CB.

Description: Quantitative RT-PCR data are analyzed using generalized linear mixed models based on lognormal-Poisson error distribution, fitted using MCMC. Control genes are not required but can be incorporated as Bayesian priors or, when template abundances correlate with conditions, as trackers of global effects (common to all genes). The package also implements a lognormal model for higher-abundance data and a "classic" model involving multi-gene normalization on a by-sample basis. Several plotting functions are included to extract and visualize results. The detailed tutorial is available here: <http://bit.ly/1Nwo4CB>.

Description: Quantitative RT-PCR data are analyzed using generalized linear mixed models based on lognormal-Poisson error distribution, fitted using MCMC. Control genes are not required but can be incorporated as Bayesian priors or, when template abundances correlate with conditions, as trackers of global effects (common to all genes). The package also implements a lognormal model for higher-abundance data and a "classic" model involving multi-gene normalization on a by-sample basis. Several plotting functions are included to extract and visualize results. The detailed tutorial is available here: <https://matzlab.weebly.com/uploads/7/6/2/2/76229469/mcmc.qpcr.tutorial.v1.2.4.pdf>.

Title: Posterior Distribution of Extreme Value Models in R
Authors@R: c(person("Fernando Ferraz", "do Nascimento", email =
"fernandofn@ufpi.edu.br", role = c("aut", "cre")),
person("Wyara Vanesa", "Moura e Silva", email = "wyaravms@hotmail.com",
Author: Fernando Ferraz do Nascimento [aut, cre], Wyara Vanesa Moura e Silva [aut, ctb]
Maintainer: Fernando Ferraz do Nascimento <fernandofn@ufpi.edu.br>
Description: Provides some function to perform posterior estimation for some distribution, with emphasis to extreme value distributions.It contains some extreme datasets, and functions that perform the runs of posterior points of the GPD and GEV distribution. The package calculate some important extreme measures like return level for each t periods of time, and some plots as the predictive distribution, and return level plots.