A toolbox to assist with statistical analysis of animal trajectories.
    It provides simple access to algorithms for calculating and assessing a variety of 
    characteristics such as speed and acceleration, as well as multiple measures of 
    straightness or tortuosity. Some support is provided for 3-dimensional trajectories. 
    McLean & Skowron Volponi (2018) <doi:10.1111/eth.12739>.

Getting 'TikTok' data (<https://www.tiktok.com/>) through the official 
  and unofficial APIs—in other words, you can track 'TikTok'.

Formula-based user-interfaces to specific transformation models
  implemented in package 'mlt' (<DOI:10.32614/CRAN.package.mlt>, <DOI:10.32614/CRAN.package.mlt.docreg>). 
  Available models include Cox models, some parametric 
  survival models (Weibull, etc.), models for ordered categorical variables,
  normal and non-normal (Box-Cox type) linear models, and continuous outcome logistic regression
  (Lohse et al., 2017, <DOI:10.12688/f1000research.12934.1>). The underlying theory
  is described in Hothorn et al. (2018) <DOI:10.1111/sjos.12291>. An extension to
  transformation models for clustered data is provided (Barbanti and Hothorn, 2022,
  <DOI:10.1093/biostatistics/kxac048>) and a tutorial explains applications in survival analysis
  (Siegfried et al., 2025, <DOI:10.48550/arXiv.2402.06428>). Multivariate conditional transformation models 
  (Klein et al, 2022, <DOI:10.1111/sjos.12501>) and shift-scale transformation models (Siegfried et al, 2023,
  <DOI:10.1080/00031305.2023.2203177>) can be fitted as well. The package contains an implementation of 
  a doubly robust score test, described in  Kook et al. (2024, <DOI:10.1080/01621459.2024.2395588>).

Extends invariant causal prediction (Peters et al., 2016, 
    <doi:10.1111/rssb.12167>) to generalized linear and transformation models 
    (Hothorn et al., 2018, <doi:10.1111/sjos.12291>).
    The methodology is described in Kook et al. (2023,
    <doi:10.1080/01621459.2024.2395588>).

Set of sequence analysis tools for manipulating, describing and rendering categorical sequences, and more generally mining sequence data in the field of social sciences. Although this sequence analysis package is primarily intended for state or event sequences that describe time use or life courses such as family formation histories or professional careers, its features also apply to many other kinds of categorical sequence data. It accepts many different sequence representations as input and provides tools for converting sequences from one format to another. It offers several functions for describing and rendering sequences, for computing distances between sequences with different metrics (among which optimal matching), original dissimilarity-based analysis tools, and functions for extracting the most frequent event subsequences and identifying the most discriminating ones among them. A user's guide can be found on the TraMineR web page.