Provides convenient tools for visualising ordinal outcome data following the "Grotta Bar" approach pioneered by The National Institute of Neurological Disorders and Stroke rt-PA Stroke Study Group (1995) <doi:10.1056/NEJM199512143332401>.

Principal Component Analysis (PCA) is a statistical technique used to reduce the dimensionality of a dataset while preserving as much variability as possible. By transforming the original variables into a new set of uncorrelated variables called principal components, PCA helps in identifying patterns and simplifying the complexity of high-dimensional data. The 'RankPCA' package provides a streamlined workflow for performing PCA on datasets containing both categorical and continuous variables. It facilitates data preprocessing, encoding of categorical variables, and computes PCA to determine the optimal number of principal components based on a specified variance threshold. The package also computes composite indices for ranking observations, which can be useful for various analytical purposes. Garai, S., & Paul, R. K. (2023) <doi:10.1016/j.iswa.2023.200202>.

Statistical tools for the Mallows-Binomial model, the first joint statistical model for preference learning for rankings and ratings.

Methods for ranking responses of a single response question or a 
  multiple response question are described in the two papers: 
  1. Wang, H. (2008). Ranking Responses in Multiple-Choice Questions. Journal of 
  Applied Statistics, 35, 465-474. <DOI:10.1080/02664760801924533> 
  2. Wang, H. and Huang, W. H. (2014). Bayesian Ranking Responses in Multiple 
  Response Questions. Journal of the Royal Statistical Society: Series A 
  (Statistics in Society), 177, 191-208. <DOI:10.1111/rssa.12009>.

Implementation of Kernelized score functions and other semi-supervised learning algorithms for node label ranking to analyze  biomolecular networks. RANKS can be easily applied to a large set of different relevant problems in computational biology, ranging from automatic protein function prediction, to gene disease prioritization and drug repositioning, and more in general to any bioinformatics problem that can be formalized as a node label ranking problem in a graph. The modular nature of the implementation allows to experiment with different score functions and kernels and to easily compare the results with baseline network-based methods such as label propagation and random walk algorithms, as well as to enlarge the algorithmic scheme by adding novel user-defined score functions and kernels.