Provides a visualization for characterizing subgroups defined by a decision tree structure. The visualization simplifies the ability to interpret individual pathways to subgroups; each sub-plot describes the distribution of observations within individual terminal nodes and percentile ranges for the associated inner nodes.

Visualize and compute percentiles/probabilities of normal, t, f, chi square 
    and binomial distributions.

Estimates and plots as a heat map the correlation coefficients obtained via the wavelet local multiple correlation 'WLMC' (Fernández-Macho 2018) and the 'dominant' variable/s, i.e., the variable/s that maximizes the multiple correlation through time and scale (Polanco-Martínez et al. 2020, Polanco-Martínez 2022). We improve the graphical outputs of WLMC proposing a didactic and useful way to visualize the 'dominant' variable(s) for a set of time series. The WLMC was designed for financial time series, but other kinds of data (e.g., climatic, ecological, etc.) can be used. The functions contained in 'VisualDom' are highly flexible since these contains several parameters to personalize the time series under analysis and the heat maps. In addition, we have also included two data sets (named 'rdata_climate' and 'rdata_Lorenz') to exemplify the use of the functions contained in 'VisualDom'. Methods derived from Fernández-Macho (2018) <doi:10.1016/j.physa.2017.11.050>, Polanco-Martínez et al. (2020) <doi:10.1038/s41598-020-77767-8> and Polanco-Martínez (2023, in press).

A collection of tools for analyzing the field of vision. It
    provides a framework for development and use of innovative methods for
    visualization, statistical analysis, and clinical interpretation of
    visual-field loss and its change over time. It is intended to be a tool for
    collaborative research. The package is described in Marin-Franch and Swanson
    (2013) <doi:10.1167/13.4.10> and is part of the Open Perimetry Initiative
    (OPI) [Turpin, Artes, and McKendrick (2012) <doi:10.1167/12.11.22>].