Implements the Analytic Hierarchy Process (AHP) method using Gaussian normalization (AHPGaussian) 
    to derive the relative weights of the criteria and alternatives. It also includes functions for visualizing 
    the results and generating graphical outputs. Method as described in: 
    dos Santos, Marcos (2021) <doi:10.13033/ijahp.v13i1.833>.

The AHP method (Analytic Hierarchy Process) is a multi-criteria decision-making method addressing
             choice and outranking problems. The method enables to perform the analysis of alternatives in each
             type of criterion and then provides a global performance of each alternative in the decision context.
             The main difference of this package is the possibility of evaluating the alternatives using quantitative
             data, by numerical representation, and qualitative data, using the Saaty scale, providing preference
             relation between variables by a pairwise evaluation.

An integrated set of functions for building, analyzing, and visualizing 
 Analytic Hierarchy Process (AHP) models, designed to support structured decision-making
 in consultancy, policy analysis, and research (Bose 2022 <doi:10.1002/mcda.1784>; 
 Bose 2023 <doi:10.1002/mcda.1821>). In addition to tools for assessing and improving the 
 consistency of pairwise comparison matrices (PCMs), the package supports 
 full-hierarchy weight computation, intuitive tree-based visualization, sensitivity 
 analysis, along with convenient PCM generation from user preferences.

Implementation of a hybrid MCDM method build from the AHP (Analytic Hierarchy Process) and TOPSIS-2N (Technique for Order of Preference by Similarity to Ideal Solution - with two normalizations). This method is described in Souza et al. (2018) <doi: 10.1142/S0219622018500207>.

Compute a tree level hierarchy, judgment matrix, consistency index and ratio, priority vectors, hierarchic synthesis and rank. Based on the book entitled "Models, Methods, Concepts and Applications of the Analytic Hierarchy Process" by Saaty and Vargas (2012, ISBN 978-1-4614-3597-6).