Provides a flexible alternative to the built-in rank() function called smartrank(). 
    Optionally rank categorical variables by frequency (instead of in alphabetical order), and control whether ranking is based on descending/ascending order. 
    smartrank() is suitable for both numerical and categorical data.

Performs aggregation of ordered lists based
        on the ranks using several different algorithms:
        Cross-Entropy Monte Carlo algorithm, Genetic algorithm, and a
        brute force algorithm (for small problems).

Easily compute an aggregate ranking (also called a median ranking or a
             consensus ranking) according to the axiomatic approach presented
             by Cook et al. (2007). This approach minimises the number of violations
             between all candidate consensus rankings and all input (partial) rankings,
             and draws on a branch and bound algorithm and a heuristic algorithm to
             drastically improve speed. The package also provides an option to bootstrap
             a consensus ranking based on resampling input rankings (with 
             replacement). Input rankings can be either incomplete (partial) or complete.
             Reference: Cook, W.D., Golany, B., Penn, M. and Raviv, T. (2007) <doi:10.1016/j.cor.2005.05.030>.

Polynomially bounded algorithms to aggregate complete rankings under Kemeny's axiomatic framework. 'RankAggSIgFUR' (pronounced as rank-agg-cipher) contains two heuristics algorithms: FUR and SIgFUR. For details, please see Badal and Das (2018) <doi:10.1016/j.cor.2018.06.007>.

Implementation of a model-based clustering algorithm for
    ranking data (C. Biernacki, J. Jacques (2013) <doi:10.1016/j.csda.2012.08.008>). 
    Multivariate rankings as well as partial rankings are taken
    into account. This algorithm is based on an extension of the Insertion
    Sorting Rank (ISR) model for ranking data, which is a meaningful and
    effective model parametrized by a position parameter (the modal ranking,
    quoted by mu) and a dispersion parameter (quoted by pi). The heterogeneity
    of the rank population is modelled by a mixture of ISR, whereas conditional
    independence assumption is considered for multivariate rankings.