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.

Provides functions for generating partially
    replicated (p-rep) test-control designs for early
    generation varietal trials conducted across multiple
    environments. The package implements three construction
    methods for obtaining efficient p-rep test-control
    designs with one or more control treatments. The package
    extends the partially replicated design framework of
    Vinaykumar et al. (2026) <doi:10.1007/s12355-025-01684-1>
    to accommodate test-control comparisons in breeding
    trials. Functions are also provided for generating
    randomized and non-randomized layouts and for evaluating
    designs using average variance measures for
    test-versus-test and test-versus-control comparisons,
    together with canonical efficiency factors. The proposed
    designs are useful for large-scale varietal evaluation
    trials where a large number of test lines are assessed
    under limited experimental resources.