Pre-made models that can be rapidly tailored to various chemicals
             and species using chemical-specific in vitro data and physiological 
             information. These tools allow incorporation of chemical 
             toxicokinetics ("TK") and in vitro-in vivo extrapolation ("IVIVE") 
             into bioinformatics, as described by Pearce et al. (2017) 
             (<doi:10.18637/jss.v079.i04>). Chemical-specific 
             in vitro data characterizing toxicokinetics can be been obtained 
             from relatively high-throughput experiments. The 
             chemical-independent
             ("generic") physiologically-based ("PBTK") and empirical 
             (for example, one compartment) "TK" models included here can be 
             parameterized with in vitro data or in silico predictions which are 
             provided for thousands of chemicals, multiple exposure routes, 
             and various species. The models are systems of ordinary 
             differential equations that are solved
             using compiled (C-based) code for speed. A Monte Carlo sampler is
             included for simulating human biological variability
             (Ring et al., 2017 <doi:10.1016/j.envint.2017.06.004>)
             and propagating parameter uncertainty 
             (Wambaugh et al., 2019 <doi:10.1093/toxsci/kfz205>). 
             Empirically calibrated methods are included for predicting 
             tissue:plasma partition coefficients and volume of distribution  
             (Pearce et al., 2017 <doi:10.1007/s10928-017-9548-7>).
             These functions and data provide a set of tools for using IVIVE to
             convert concentrations from high-throughput screening experiments
             (for example, Tox21, ToxCast) to real-world exposures via reverse 
             dosimetry (also known as "RTK")
             (Wetmore et al., 2015 <doi:10.1093/toxsci/kfv171>).
| Version: | 
2.2.1 | 
| Depends: | 
R (≥ 2.10) | 
| Imports: | 
deSolve, msm, data.table, survey, mvtnorm, truncnorm, stats, graphics, utils, magrittr, purrr, methods, Rdpack | 
| Suggests: | 
ggplot2, knitr, rmarkdown, R.rsp, GGally, gplots, scales, EnvStats, MASS, RColorBrewer, TeachingDemos, classInt, ks, stringr, reshape, reshape2, viridis, gmodels, colorspace, cowplot, ggrepel, dplyr, forcats, smatr, gridExtra, testthat | 
| Published: | 
2022-09-24 | 
| Author: | 
John Wambaugh  
    [aut, cre],
  Sarah Davidson  
    [aut],
  Robert Pearce  
    [aut],
  Caroline Ring  
    [aut],
  Greg Honda   [aut],
  Mark Sfeir [aut],
  Matt Linakis  
    [aut],
  Dustin Kapraun  
    [aut],
  Miyuki Breen  
    [ctb],
  Shannon Bell  
    [ctb],
  Xiaoqing Chang  
    [ctb],
  Todor Antonijevic  
    [ctb],
  Jimena Davis [ctb],
  James Sluka   [ctb],
  Nisha Sipes   [ctb],
  Barbara Wetmore  
    [ctb],
  Woodrow Setzer  
    [ctb] | 
| Maintainer: | 
John Wambaugh  <wambaugh.john at epa.gov> | 
| BugReports: | 
https://github.com/USEPA/CompTox-ExpoCast-httk | 
| License: | 
GPL-3 | 
| Copyright: | 
This package is primarily developed by employees of the U.S.
Federal government as part of their official duties and is
therefore public domain. | 
| URL: | 
https://www.epa.gov/chemical-research/rapid-chemical-exposure-and-dose-research | 
| NeedsCompilation: | 
yes | 
| Citation: | 
httk citation info  | 
| Materials: | 
README NEWS  | 
| CRAN checks: | 
httk results |