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.

Density-based clustering methods are well adapted to the clustering of high-dimensional data and enable the discovery of core groups of various shapes despite large amounts of noise. This package provides a novel density-based cluster extraction method, OPTICS k-Xi, and a framework to compare k-Xi models using distance-based metrics to investigate datasets with unknown number of clusters. The vignette first introduces density-based algorithms with simulated datasets, then presents and evaluates the k-Xi cluster extraction method. Finally, the models comparison framework is described and experimented on 2 genetic datasets to identify groups and their discriminating features. The k-Xi algorithm is a novel OPTICS cluster extraction method that specifies directly the number of clusters and does not require fine-tuning of the steepness parameter as the OPTICS Xi method. Combined with a framework that compares models with varying parameters, the OPTICS k-Xi method can identify groups in noisy datasets with unknown number of clusters. Results on summarized genetic data of 1,200 patients are in Charlon T. (2019) <doi:10.13097/archive-ouverte/unige:161795>. A short video tutorial can be found at <https://www.youtube.com/watch?v=P2XAjqI5Lc4/>.