A series of additional Tcl commands and Tk widgets to supplement the tcltk package.

Provides functions for robust trimmed clustering. The methods are 
    described in Garcia-Escudero (2008) <doi:10.1214/07-AOS515>, 
    Fritz et al. (2012) <doi:10.18637/jss.v047.i12>, 
    Garcia-Escudero et al. (2011)  <doi:10.1007/s11222-010-9194-z> and others.

The 1311 time series from the tourism forecasting competition conducted in 2010 and described in Athanasopoulos et al. (2011) <DOI:10.1016/j.ijforecast.2010.04.009>.

The ToxCast Data Analysis Pipeline ('tcpl') is an R package that manages, curve-fits, plots, and stores ToxCast data to populate its linked MySQL database, 'invitrodb'. The package was developed for the chemical screening data curated by the US EPA's Toxicity Forecaster (ToxCast) program, but 'tcpl' can be used to support diverse chemical screening efforts.

The tcplfit2 R package performs basic concentration-response curve fitting. The original tcplFit() function in the tcpl R package performed basic concentration-response curvefitting to 3 models. With tcplfit2, the core tcpl concentration-response functionality has been expanded to process diverse high-throughput screen (HTS) data generated at the US Environmental Protection Agency, including targeted ToxCast, high-throughput transcriptomics (HTTr) and high-throughput phenotypic profiling (HTPP). tcplfit2 can be used independently to support analysis for diverse chemical screening efforts.

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>.