In some phase I trials, the design goal is to find the dose associated with a certain target toxicity rate or the dose with a certain weighted sum of rates of various toxicity grades. 'TITEgBOIN' provides the set up and calculations needed to run a dose-finding trial using bayesian optimal interval (BOIN) (Yuan et al. (2016) <doi:10.1158/1078-0432.CCR-16-0592>), generalized bayesian optimal interval (gBOIN) (Mu et al. (2019) <doi:10.1111/rssc.12263>), time-to-event bayesian optimal interval (TITEBOIN) (Lin et al. (2020) <doi:10.1093/biostatistics/kxz007>) and time-to-event generalized bayesian optimal interval (TITEgBOIN) (Takeda et al. (2022) <doi:10.1002/pst.2182>) designs. 'TITEgBOIN' can conduct tasks: run simulations and get operating characteristics; determine the dose for the next cohort; select maximum tolerated dose (MTD). These functions allow customization of design characteristics to vary sample size, cohort sizes, target dose limiting toxicity (DLT) rates or target normalized equivalent toxicity score (ETS) rates to account for discrete toxicity score, and incorporate safety and/or stopping rules.

Functions to design phase 1 trials using an isotonic regression based design incorporating time-to-event information. Simulation and design functions are available, which incorporate information about followup and DLTs, and apply isotonic regression to devise estimates of DLT probability.

A collection of functions to plot acid/base titration 
    curves (pH vs. volume of titrant), complexation titration curves 
    (pMetal vs. volume of EDTA), redox titration curves (potential 
    vs.volume of titrant), and precipitation titration curves (either 
    pAnalyte or pTitrant vs. volume of titrant). Options include the 
    titration of mixtures, the ability to overlay two or more 
    titration curves, and the ability to show equivalence points.

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.