htmltools (>= 0.3.6), htmlwidgets (>= 1.3), magrittr,
crosstalk, promises

person("David C.", "Norris"
, role = c("aut", "cre")
, email = "david@precisionmethods.guru"
)
km.ci, pomp, Hmisc, data.table, dplyr, r2d3, shiny, jsonlite,
methods
knitr, rmarkdown, lattice, latticeExtra, widgetframe, tidyr,
RColorBrewer
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
Norris (2017) <doi:10.1101/240846>.

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.
Please see Norris (2017) <doi:10.12688/f1000research.10624.3> and
Norris (2017) <doi:10.1101/240846>.

Diagnostic Test Assessment in the Absence of Gold Standard
To calculate the sensitivity and specificity in the absence of gold standard using the Bayesian method.
The Bayesian method can be referenced at Haiyan Gu and Qiguang Chen (1999) <doi:10.3969/j.issn.1002-3674.1999.04.004>.

Creates Plots Accompanying Bayesian Diagnostic Test Accuracy
Meta-Analyses
c(person("Ian", "Schiller", role = c("aut", "cre"),
email = "ian.schiller@rimuhc.ca"),
person("Nandini", "Dendukuri",email="nandini.dendukuri@mcgill.ca", role = "ctb") )
Function to create forest plots. Functions to use posterior samples from Bayesian bivariate meta-analysis model, Bayesian hierarchical summary receiver operating characteristic (HSROC) meta-analysis model or Bayesian latent class (LC) meta-analysis model to create Summary Receiver Operating Characteristic (SROC) plots using methods described by Harbord et al (2007)<doi:10.1093/biostatistics/kxl004>.
Ian Schiller [aut, cre],
Nandini Dendukuri [ctb]