Description: Base package for 'outsider' <https://github.com/ropensci/outsider>.
The 'outsider' package and its sister packages enable the installation and
running of external, command-line software within R. This base package is a
key dependency of the user-facing 'outsider' package as it provides the
utilities for interfacing between 'Docker' <https://www.docker.com> and R.
It is intended that end-users of 'outsider' do not directly work with this
Imports: utils (>= 3.1), crayon, devtools (>= 1.1), jsonlite (>= 1.1),
Author: Dom Bennett [aut, cre] (<https://orcid.org/0000-0003-2722-1359>),

Imports: utils (>= 3.1), crayon, devtools (>= 1.1), pkgload, sys (>=
Packaged: 2021-04-17 10:22:53 UTC; dominicbennett

Title: Model of Adaptive Trait Evolution
Description: Performs statistical inference on the models of adaptive trait evolution under approximate Bayesian computation. This can simulate traits from four models, compute trait data summary statistics. Parameters are estimated under Approximate Bayesian Computation, model selection as well as posterior parameter mean will be reported. Users need to enter a comparative dataset and a phylogenetic tree.
Imports: stats, ape, coda, Sim.DiffProc, MCMCpack, abc, phytools, nlme,
TreeSim, adephylo, maps, geiger, EasyABC, utils

Title: Overdispersion in Count Data Multiple Regression Analysis
Authors@R: c(person(given = "Rafael", family = "Freitas Souza", role = "cre", email = "fsrafael@usp.br"), person(given = "Luiz Paulo", family = "Favero", role = "ctb"), person(given = "Patricia", family = "Belfiore", role = "ctb"), person(given = "Hamilton Luiz", family = "Correa", role = "ctb"), person(given = "A. Colin", family = "Cameron", role = "aut"), person(given = "Pravin", family = "Trivedi", role = "aut"))
Maintainer: Rafael Freitas Souza <fsrafael@usp.br>
Description: Detection of overdispersion in count data for multiple regression analysis.
Log-linear count data regression is one of the most popular techniques for predictive
modeling where there is a non-negative discrete quantitative dependent variable. In
order to ensure the inferences from the use of count data models are appropriate,
researchers may choose between the estimation of a Poisson model and a negative binomial
model, and the correct decision for prediction from a count data estimation is directly
linked to the existence of overdispersion of the dependent variable, conditional to the
explanatory variables. Based on the studies of Cameron and Trivedi (1990)
the overdisp() command is a contribution to researchers, providing a fast and secure
solution for the detection of overdispersion in count data. Another advantage is that
the installation of other packages is unnecessary, since the command runs in the basic