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.

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.

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.

Create disposable R packages for testing.
    You can create, install and load multiple R packages with a single
    function call, and then unload, uninstall and destroy them with another
    function call. This is handy when testing how some R code or an R package
    behaves with respect to other packages.

Evaluate the presence of disposition effect and others irrational
    investor's behaviors based solely on investor's transactions and financial
    market data. Experimental data can also be used to perform the analysis.  
    Four different methodologies are implemented to account for the different
    nature of human behaviors on financial markets.  
    Novel analyses such as portfolio driven and time series disposition effect
    are also allowed.