Suggests: tcltk, xtable, kohonen, mclust

Suggests: xtable, kohonen, mclust, clValid

Description: Performs aggregation of ordered lists based
on the ranks using several different algorithms:
brute force algorithm (for small problems).

Title: Aggregation of (Partial) Ordinal Rankings
Description: 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>.

Title: Ranking Responses in a Single Response Question or a Multiple
Description: Methods for ranking responses of a single response question or a multiple response question

Author: Hsiuying Wang, Yu-Chun Lin, Wan-Ting Huang
Description: 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
2. Wang, H. and Huang, W. H. (2014). Bayesian Ranking Responses in Multiple
Response Questions. Journal of the Royal Statistical Society: Series A

Title: Model-based clustering for multivariate partial ranking data
ranking data. 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.
Collate: 'conversion.R' 'likelihood.R' 'mixtureSEM.R' 'rankclust.R'
Packaged: 2013-08-28 15:54:53 UTC; grimonprez