person("Xianying", "Tan", role = "aut"),
person("Maximilian", "Girlich", role = "ctb"),
person("Greg", "Freedman Ellis", role = "ctb"),
person("Johannes", "Rauh", role = "ctb"),
Imports: htmltools (>= 0.3.6), htmlwidgets (>= 1.3), jsonlite (>=
Suggests: knitr (>= 1.8), rmarkdown, shiny (>= 1.1.0)

Suggests: knitr (>= 1.8), rmarkdown, shiny (>= 1.2.0)

Suggests: knitr (>= 1.8), rmarkdown, shiny (>= 1.2.0), testit

Suggests: knitr (>= 1.8), rmarkdown, shiny (>= 1.6), bslib, testit

person("Alex", "Pickering", role = c("ctb")),
0.9.16), magrittr, crosstalk, jquerylib, promises

Imports: htmltools (>= 0.3.5), htmlwidgets (>= 0.6), magrittr
Suggests: jsonlite (>= 0.9.16), knitr (>= 1.8), rmarkdown, shiny (>=

person("William", "Holmes", role = c("ctb")),

person("Mikko", "Marttila", role = c("ctb")),

person("Andres", "Quintero", role = c("ctb")),
Suggests: knitr (>= 1.8), rmarkdown, shiny (>= 1.6), bslib, future,

person("Stéphane", "Laurent", role = c("ctb")),

person("Xianying", "Tan", role = "ctb"),
Imports: htmltools (>= 0.3.6), htmlwidgets (>= 0.6), magrittr,

Imports: htmltools (>= 0.3.6), htmlwidgets (>= 1.0), magrittr,

Imports: htmltools (>= 0.3.6), htmlwidgets (>= 1.3), magrittr,

Maintainer: David C. Norris <david@precisionmethods.guru>
Imports: km.ci, pomp, Hmisc, data.table, dplyr, r2d3, shiny, jsonlite,
Suggests: knitr, rmarkdown, lattice, latticeExtra, widgetframe, tidyr,
Description: DTAT is a methodologic framework allowing dose individualization to be
conceived as a continuous learning process that begins in early-phase
clinical trials and continues throughout drug development, on into
clinical practice. This package includes code that researchers may use
to reproduce or extend key results of the DTAT research programme, plus
tools for trialists to design and simulate a '3+3/PC' dose-finding study.
Please see Norris (2017) <doi:10.12688/f1000research.10624.3> and

Description: Dose Titration Algorithm Tuning (DTAT) is a methodologic framework
allowing dose individualization to be conceived as a continuous
learning process that begins in early-phase clinical trials and
continues throughout drug development, on into clinical practice.
This package includes code that researchers may use to reproduce
or extend key results of the DTAT research programme, plus tools
for trialists to design and simulate a '3+3/PC' dose-finding study.