Michael G. SchimekEva Budinska, Jie Ding, Karl G. Kugler, Vendula Svendova, Shili Lin

Inference, Aggregation and Visualization for Top-K Ranked Lists
Michael G. Schimek, Eva Budinska, Jie Ding, Karl G. Kugler, Vendula Svendova, Shili Lin

Michael G. Schimek, Eva Budinska, Jie Ding, Karl G. Kugler, Vendula Svendova, Shili Lin, Bastian Pfeifer
For multiple ranked input lists (full or partial) representing the same set of N objects, the package 'TopKLists' <doi:10.1515/sagmb-2014-0093> offers (1) statistical inference on the lengths of informative top-k lists, (2) stochastic aggregation of full or partial lists, and (3) graphical tools for the statistical exploration of input lists, and for the visualization of aggregation results. Note that RGtk2 and gWidgets2RGtk2 have been archived on CRAN. See <https://github.com/pievos101/TopKLists> for installation instructions.

Multi-Criteria Method for Decision (TOPSIS)
person("Wagner", "Martins dos Santos", , "wagnnerms97@gmail.com", role = c("aut", "cre"),
comment = c(ORCID = "0000-0002-3584-1323"))
Assists in the TOPSIS analysis process, designed to return at the end of the answer of the TOPSIS multicriteria analysis,  a ranking table with the best option as the analysis proposes. TOPSIS is basically a technique developed by Hwang and Yoon in 1981, starting from the point that the best alternative should be closest to the positive ideal solution and farthest from the negative one, based on several criteria to result in the best benefit. (LIU, H. et al., 2019) <doi:10.1016/j.agwat.2019.105787>.
Wagner Martins dos Santos [aut, cre]
(<https://orcid.org/0000-0002-3584-1323>)
Wagner Martins dos Santos <wagnnerms97@gmail.com>

The Topic SCORE Algorithm to Fit Topic Models
c(person("Minzhe", "Wang", role = c("aut", "cre"),
email = "minzhew@uchicago.edu"),
person("Tracy", "Ke", role = "aut"))
Provides implementation of the "Topic SCORE" algorithm that is
proposed by Tracy Ke and Minzhe Wang. The singular value decomposition
step is optimized through the usage of svds() function in 'RSpectra'
package, on a 'dgRMatrix' sparse matrix. Also provides a column-wise
error measure in the word-topic matrix A, and an algorithm for
recovering the topic-document matrix W given A and D based on
quadratic programming.
The details about the techniques are explained in the paper "A new SVD approach to optimal topic estimation" by Tracy Ke and Minzhe Wang (2017) <arXiv:1704.07016>.
utils, stats, graphics, RSpectra, combinat, quadprog, methods,
Matrix, slam