A collection of datasets containing a variety of in vitro toxicokinetic 
             measurements including -- but not limited to -- chemical fraction unbound 
             in the presence of plasma (f_up), intrinsic hepatic clearance (Clint,
             uL/min/million hepatocytes), and membrane permeability for
             oral absorption (Caco2). The datasets provided by the package were 
             processed and analyzed with the companion 'invitroTKstats' package.

A set of tools for processing and analyzing in vitro toxicokinetic
             measurements in a standardized and reproducible pipeline. The package
             was developed to perform frequentist and Bayesian estimation on a
             variety of in vitro toxicokinetic measurements including -- but not
             limited to -- chemical fraction unbound in the presence of plasma
             (f_up), intrinsic hepatic clearance (Clint,
             uL/min/million hepatocytes), and membrane permeability for
             oral absorption (Caco2). The methods provided
             by the package were described in Wambaugh et al. (2019) <doi:10.1093/toxsci/kfz205>.

Takes in vivo toxicokinetic concentration-time data and fits
    parameters of 1-compartment and 2-compartment models for each
    chemical. These methods are described in detail in "Informatics for Toxicokinetics" (2025).

Contains functions that allow Bayesian inference on a parameter of some widely-used exponential models. The functions can generate independent samples from the closed-form posterior distribution using the inverse stable prior. Inverse stable is a non-conjugate prior for a parameter of an exponential subclass of discrete and continuous data distributions (e.g. Poisson, exponential, inverse gamma, double exponential (Laplace), half-normal/half-Gaussian, etc.). The prior class provides flexibility in capturing a wide array of prior beliefs (right-skewed and left-skewed) as modulated by a parameter that is bounded in (0,1). The generated samples can be used to simulate the prior and posterior predictive distributions. More details can be found in Cahoy and Sedransk (2019)  <doi:10.1007/s42519-018-0027-2>. The package can also be used as a teaching demo for introductory Bayesian courses.