Partial application is the process of reducing the arity of a
    function by fixing one or more arguments, thus creating a new function
    lacking the fixed arguments. The curry package provides three different ways
    of performing partial function application by fixing arguments from either
    end of the argument list (currying and tail currying) or by fixing multiple
    named arguments (partial application). This package provides this
    functionality through the %<%, %-<%, and %><% operators which allows for
    a programming style comparable to modern functional languages. Compared
    to other implementations such a purrr::partial() the operators in curry
    composes functions with named arguments, aiding in autocomplete etc.

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).

Computes the maximum likelihood estimator, the smoothed maximum likelihood
    estimator and pointwise bootstrap confidence intervals for the distribution 
    function under current status data. 
    Groeneboom and Hendrickx (2017) <doi:10.1214/17-EJS1345>.

Finds single- and two-arm designs using stochastic curtailment, as described by Law et al. (2022) <doi:10.1080/10543406.2021.2009498> 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.