Find topics in texts which are semantically embedded using techniques like word2vec or Glove. 
    This topic modelling technique models each word with a categorical distribution whose natural parameter is the inner product between a word embedding and an embedding of its assigned topic.
    The techniques are explained in detail in the paper 'Topic Modeling in Embedding Spaces' by Adji B. Dieng, Francisco J. R. Ruiz, David M. Blei (2019), available at <doi:10.48550/arXiv.1907.04907>.

Implements differential language analysis with statistical tests and offers various language visualization techniques for n-grams and topics. It also supports the 'text' package. For more information, visit <https://r-topics.org/> and <https://www.r-text.org/>.

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>.

Implements the Topic Testlet Model (TTM) as described by Xiong et al. (2025) <doi:10.1111/jedm.70001>. 
    The package integrates Latent Dirichlet Allocation (LDA) with the Partial Credit Model to 
    account for local item dependence in testlets using latent topics from student 
    textual responses.

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 Note that RGtk2 and gWidgets2RGtk2 have been archived on CRAN.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.