Disparity Filter Algorithm for Weighted Networks
c(
person("Alessandro", "Bessi", email = "alessandro.bessi@iusspavia.it", role = c("aut", "cre")),
person("Francois", "Briatte", email = "f.briatte@gmail.com", role = "aut"))
The disparity filter algorithm is a network reduction technique to
identify the 'backbone' structure of a weighted network without destroying
its multi-scale nature. The algorithm is documented in M. Angeles Serrano,
Marian Boguna and Alessandro Vespignani in "Extracting the multiscale
backbone of complex weighted networks", Proceedings of the National Academy
of Sciences 106 (16), 2009. This implementation of the algorithm supports
both directed and undirected networks.
Alessandro Bessi [aut, cre],
Francois Briatte [aut]

Simulation of Demic Diffusion with Environmental Constraints
person(given = "Jonas",
family = "Gregorio de Souza",
role = c("aut", "cre"),
email = "jonas.gregorio@gmail.com",
comment = c(ORCID = "0000-0001-6032-4443"))
Simulates demic diffusion building on models previously developed
for the expansion of Neolithic and other food-producing economies during
the Holocene (Fort et al. (2012) <doi:10.7183/0002-7316.77.2.203>, Souza et al.
(2021) <doi:10.1098/rsif.2021.0499>). Growth and emigration are modelled as
density-dependent processes using logistic growth and an asymptotic threshold
model. Environmental and terrain layers, which can change over time, affect
carrying capacity, growth and mobility. Multiple centres of origin with
their respective starting times can be specified.
Jonas Gregorio de Souza [aut, cre]
(<https://orcid.org/0000-0001-6032-4443>)
Jonas Gregorio de Souza <jonas.gregorio@gmail.com>

A package containing R functions for displaying data and
results from high-throughput screening experiments.

Functions for modelling dispersion in GLM.

Description: Functions for modelling dispersion in GLM.

Functions for estimating Gaussian dispersion regression models (Aitkin, 1987 <doi:10.2307/2347792>), overdispersed binomial logit models (Williams, 1987 <doi:10.2307/2347977>), and overdispersed Poisson log-linear models (Breslow, 1984 <doi:10.2307/2347661>), using a quasi-likelihood approach.