Statistical tools for the Mallows-Binomial model, the first joint statistical model for preference learning for rankings and ratings. This project was supported by the National Science Foundation under Grant No. 2019901.

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.

Fast computing an ensemble of rank-based trees via boosting or random forest on binary and multi-class problems. It converts continuous gene expression profiles into ranked gene pairs, for which the variable importance indices are computed and adopted for dimension reduction. Decision rules can be extracted from trees.

Finds the k nearest neighbours for every point in a
    given dataset in O(N log N) time using Arya and Mount's ANN library
    (v1.1.3). There is support for approximate as well as exact searches,
    fixed radius searches and 'bd' as well as 'kd' trees. The distance is
    computed using the L2 (Euclidean) metric. Please see package 'RANN.L1'
    for the same functionality using the L1 (Manhattan, taxicab) metric.