Authors@R: person("Andrew", "Redd", role = c("aut", "cre")
Description: Provides functions for quickly looking at summary
Imports: dplyr, methods, pkgcond, purrr, rlang, tibble, tidymargins,
Suggests: covr, forcats, datasets, dbplyr, testthat, testextra, DBI,
Author: Andrew Redd [aut, cre] (<https://orcid.org/000-0002-6149-2438>)

Description: A toolbox for developing applications, games, simulations, or
agent-based models in the R terminal. Included functions allow users to move the
cursor around the terminal screen, change text colors and attributes, clear the
screen, hide and show the cursor, map key presses to functions, draw shapes and curves,
among others. Most functionalities require users to be in a terminal (not the R GUI).

Title: Confidence Intervals for the Current Status Model
person("Piet", "Groeneboom", email = "P.Groeneboom@tudelft.nl",role = "aut"),
person("Kim", "Hendrickx", email = "kim.hendrickx@uhasselt.be",role = "cre"))
Description: Computes the maximum likelihood estimator, the smoothed maximum likelihood
estimator and pointwise bootstrap confidence intervals for the distribution
Groeneboom and Hendrickx (2017) <arXiv:1701.07359>.

Groeneboom and Hendrickx (2017) <doi:10.1214/17-EJS1345>.

Title: Finds Binary Outcome Designs Using Stochastic Curtailment
Description: Finds single- and two-arm designs using stochastic curtailment, as described by Law et al. (2019) <arXiv:1909.03017> and Law et al. (2021) <doi:10.1002/pst.2067> respectively. Designs can be single-stage or multi-stage. Non-stochastic curtailment is possible as a special case. Desired error-rates, maximum sample size and lower and upper anticipated response rates are inputted and suitable designs are returned with operating characteristics. Stopping boundaries and visualisations are also available. The package can find designs using other approaches, for example designs by Simon (1989) <doi:10.1016/0197-2456(89)90015-9> and Mander and Thompson (2010) <doi:10.1016/j.cct.2010.07.008>. Other features: compare and visualise designs using a weighted sum of expected sample sizes under the null and alternative hypotheses and maximum sample size; visualise any binary outcome design.
Imports: ggplot2, gridExtra, ggthemes, data.table, pkgcond, stats
Author: Martin Law [aut, cre] (<https://orcid.org/0000-0001-9594-348X>)