Convert T Cell Receptor (TCR) gene names between the 10X Genomics, Adaptive Biotechnologies, and ImMunoGeneTics (IMGT) nomenclatures.

Implements methods for selecting the number of factors in Poisson
  factor models, with a primary focus on Thinning Cross-Validation (TCV). The
  TCV method is based on the 'data thinning' technique, which probabilistically
  partitions each count observation into training and test sets while preserving
  the underlying factor structure. The Poisson factor model is then fit on the
  training set, and model selection is performed by comparing predictive
  performance on the test set. This toolkit is designed for researchers working
  with high-dimensional count data in fields such as genomics, text mining, and
  social sciences. The data thinning methodology is detailed in Dharamshi et al.
  (2025) <doi:10.1080/01621459.2024.2353948> and Wang et al. (2025)
  <doi:10.1080/01621459.2025.2546577>.

Framework provides functions to parse 'Training Center XML (TCX)' files
    and extract key activity metrics such as total distance, total time, calories burned,
    maximum altitude, and power values (watts). This package is useful for analyzing workout
    and training data from devices that export 'TCX' format.

The 'twelvedata' REST service offers access to current and historical
 data on stocks, standard as well as digital 'crypto' currencies, and other financial
 assets covering a wide variety of course and time spans. See <https://twelvedata.com/>
 for details, to create an account, and to request an API key for free-but-capped access
 to the data.

Tools for Topological Data Analysis. The package focuses on statistical analysis of persistent homology and density clustering. For that, this package provides an R interface for the efficient algorithms of the C++ libraries 'GUDHI' <https://project.inria.fr/gudhi/software/>, 'Dionysus' <https://www.mrzv.org/software/dionysus/>, and 'PHAT' <https://bitbucket.org/phat-code/phat/>. This package also implements methods from Fasy et al. (2014) <doi:10.1214/14-AOS1252> and Chazal et al. (2015) <doi:10.20382/jocg.v6i2a8>  for analyzing the statistical significance of persistent homology features.