Data collected on movement behavior is often in the form of time-
    stamped latitude/longitude coordinates sampled from the underlying movement
    behavior. These data can be compressed into a set of segments via the Top-
    Down Time Ratio Segmentation method described in Meratnia and de By (2004)
    <doi:10.1007/978-3-540-24741-8_44> which, with some loss of information, 
    can both reduce the size of the data as well as provide corrective smoothing
    mechanisms to help reduce the impact of measurement error.  This is an
    improvement on the well-known Douglas-Peucker algorithm for segmentation
    that operates not on the basis of perpendicular distances. Top-Down Time
    Ratio segmentation allows for disparate sampling time intervals by
    calculating the distance between locations and segments with respect to
    time. Provided a trajectory with timestamps, tdtr() returns a set of straight-
    line segments that can represent the full trajectory. McCool, Lugtig,
    and Schouten (2022) <doi:10.1007/s11116-022-10328-2> describe this method as
    implemented here in more detail.

Calculates topic-specific diagnostics (e.g. mean token length, exclusivity) for 
    Latent Dirichlet Allocation and Correlated Topic Models fit using the 'topicmodels' package.
    For more details, see Chapter 12 in Airoldi et al. (2014, ISBN:9781466504080), 
    pp 262-272 Mimno et al. (2011, ISBN:9781937284114), and Bischof et al. (2014) <arXiv:1206.4631v1>.

Leveraging (large) language models for automatic topic labeling. The main function converts a list of top terms into a label for each topic. Hence, it is complementary to any topic modeling package that produces a list of top terms for each topic. While human judgement is indispensable for topic validation (i.e., inspecting top terms and most representative documents), automatic topic labeling can be a valuable tool for researchers in various scenarios.

Provides an interface to the C code for Latent Dirichlet
	     Allocation (LDA) models and Correlated Topics Models
	     (CTM) by David M. Blei and co-authors and the C++ code
	     for fitting LDA models using Gibbs sampling by Xuan-Hieu
	     Phan and co-authors.