When visualising changes between two values over time, a strict
 linear interpolation can look jarring and unnatural.  By applying a non-linear
 easing to the transition, the motion between values can appear smoother and 
 more natural.  This package includes functions for applying such non-linear
 easings to colors and numeric values, and is useful where smooth animated movement
 and transitions are desired.

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.

A modular package for measuring disparity (multidimensional space occupancy). Disparity can be calculated from any matrix defining a multidimensional space. The package provides a set of implemented metrics to measure properties of the space and allows users to provide and test their own metrics. The package also provides functions for looking at disparity in a serial way (e.g. disparity through time) or per groups as well as visualising the results. Finally, this package provides several statistical tests for disparity analysis.