Facilitate the management of data from knowledge
   resources that are frequently used alone or together
   in research environments.
   In 'TKCat', knowledge resources are manipulated as modeled database (MDB)
   objects. These objects provide access to the data tables along with a general
   description of the resource and a detail data model documenting the
   tables, their fields and their relationships.
   These MDBs are then gathered in catalogs that can be easily
   explored an shared.
   Finally, 'TKCat' provides tools to easily subset, filter and combine MDBs and
   create new catalogs suited for specific needs.

A 'Tcl/Tk' Graphical User Interface (GUI) to display images than can be zoomed and panned using the mouse and keyboard shortcuts. 'tkImgR' read and write different image formats (PPM/PGM, PNG and GIF) using the standard 'Tcl/Tk' distribution (>=8.6), but other formats (JPEG, TIFF, CR2) can be handled using the 'tkImg' package for 'Tcl/Tk'.

Simple mechanism for placing R graphics in a Tk widget.

Implements inverse and augmented inverse probability weighted 
    estimators for common treatment effect parameters at an interim analysis 
    with time-lagged outcome that may not be available for all enrolled subjects.  
    Produces estimators, standard errors, and information that can be used to 
    compute stopping boundaries using software that assumes that the 
    estimators/test statistics have independent increments. 
    Tsiatis, A. A. and Davidian, M., (2022) <doi:10.1002/sim.9580> .

Implements a semiparametric estimator for the odds ratio model with
    censored, time-lagged, ordered categorical outcome in a randomized clinical
    trial that incorporates baseline and time-dependent information. 
    Tsiatis AA, Davidian M, Holloway ST (2023) <doi:10.1111/biom.13603>.

Computes the solution path of the Terminating-LARS (T-LARS) algorithm. The T-LARS algorithm 
    is a major building block of the T-Rex selector (see R package 'TRexSelector').
    The package is based on the papers Machkour, Muma, and Palomar (2022) <arXiv:2110.06048>, Efron, Hastie, Johnstone, 
    and Tibshirani (2004) <doi:10.1214/009053604000000067>, and Tibshirani (1996) <doi:10.1111/j.2517-6161.1996.tb02080.x>.