Provides functions for hormesis screening by classifying the shapes of dose-response curves based on semiparametric tests. The shapes are indications of different potential toxicology effect. It also offers a scalable visualization scheme to present testing conclusions for large-scale dataset with a large number of dose-response curves. For more information, see Jin et al. (2026) <https://github.com/YinglJin-0203/shorm/blob/main/Manuscripts/BotanicalHormesisTestingFinalDraft.docx>.

Integrates clipboard copied data in R Studio, loads and installs libraries within a R script and returns all valid arguments of a selected function.

Performs automatic creation of short forms of scales with an 
    ant colony optimization algorithm and a Tabu search. As implemented in the 
    package, the ant colony algorithm randomly selects items to build a model of 
    a specified length, then updates the probability of item selection according 
    to the fit of the best model within each set of searches. The algorithm 
    continues until the same items are selected by multiple ants a given number 
    of times in a row. On the other hand, the Tabu search changes one parameter at
    a time to be either free, constrained, or fixed while keeping track of the
    changes made and putting changes that result in worse fit in a "tabu" list
    so that the algorithm does not revisit them for some number of searches. 
    See Leite, Huang, & Marcoulides (2008) <doi:10.1080/00273170802285743> for
    an applied example of the ant colony algorithm, and Marcoulides & Falk (2018)
    <doi:10.1080/10705511.2017.1409074> for an applied example of the Tabu search.

Implement different Item Response Theory (IRT) based procedures for the development of tests from item bank. The procedures are flexible enough to be adopted for the development of short forms of full-length tests. Different procedures are considered (Epifania, Anselmi & Robusto, 2022 <doi:10.1007/978-3-031-27781-8_7> and Epifania & Finos, 2025 <doi:10.1007/978-3-031-95995-0_32>).
        The main difference between the presented procedures refers to the degree of control that they allow for targeting specific latent trait levels. The simplest procedure, denoted as benchmark procedure, does not allow for any control on the latent trait levels of interest, while the other procedures allow for specifying either discrete latent trait levels for which the information needs to be maximized (theta-target procedure, <doi:10.1007/978-3-031-27781-8_7>) or a target information function that needs to be recreated with the selected items (item selection algorithm -ISA- denoted as Frank in <doi:10.1007/978-3-031-95995-0_32>).
        Another difference concerns the definition of the number of items to be selected. In the benchmark and theta-target procedures, the number of items must be defined a priori, while in ISA the number of items is determined automatically by the algorithm.