Optimal group-sequential designs minimise some function of the expected and maximum sample size whilst controlling the type I error rate and power at a specified level. 'OptGS' provides functions to quickly search for near-optimal group-sequential designs for normally distributed outcomes. The methods used are described in Wason, JMS (2015) <doi:10.18637/jss.v066.i02>.

Estimation of value and hedging strategy of call and put options, based on optimal hedging and Monte Carlo method, from Chapter 3 of 'Statistical Methods for Financial Engineering', by Bruno Remillard, CRC Press, (2013).

Predictive scores must be updated with care, because actions taken on the basis of existing risk scores causes bias in risk estimates from the updated score. A holdout set is a straightforward way to manage this problem: a proportion of the population is 'held-out' from computation of the previous risk score. This package provides tools to estimate a size for this holdout set and associated errors. Comprehensive vignettes are included. Please see: Haidar-Wehbe S, Emerson SR, Aslett LJM, Liley J (2022) <doi:10.48550/arXiv.2202.06374> (in Annals of Applied Statistics) for details of methods.

Implements a simulation study to assess the strengths and
    weaknesses of causal inference methods for estimating policy effects
    using panel data. See Griffin et al. (2021)
    <doi:10.1007/s10742-022-00284-w> and Griffin et al. (2022)
    <doi:10.1186/s12874-021-01471-y> for a description of our methods.

The restricted optimal design method is implemented to optimally 
 allocate a set of items that require calibration to a group of examinees.
 The optimization process is based on the method described in detail by 
 Ul Hassan and Miller in their works published in (2019) 
 <doi:10.1177/0146621618824854> and (2021) <doi:10.1016/j.csda.2021.107177>.
 To use the method, preliminary item characteristics must be provided as input.
 These characteristics can either be expert guesses or based on previous
 calibration with a small number of examinees. The item characteristics
 should be described in the form of parameters for an Item Response
 Theory (IRT) model. These models can include the Rasch model, the
 2-parameter logistic model, the 3-parameter logistic model, or a mixture
 of these models. The output consists of a set of rules for each item
 that determine which examinees should be assigned to each item. The
 efficiency or gain achieved through the optimal design is quantified by
 comparing it to a random allocation. This comparison allows for an
 assessment of how much improvement or advantage is gained by using the
 optimal design approach. This work was supported by the Swedish Research Council
 (Vetenskapsrådet) Grant 2019-02706.