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 static short test forms (STFs) from a test. Two main procedures are considered, specifically the typical IRT-based procedure for the development of STF, and a recently introduced procedure (Epifania, Anselmi & Robusto, 2022 <doi:10.1007/978-3-031-27781-8_7>).
        The procedures differ in how the most informative items are selected for the inclusion in the STF, either by considering their item information functions without considering any specific level of the latent trait (typical procedure) or by considering their informativeness with respect to specific levels of the latent trait, denoted as theta targets (the newly introduced procedure). Regarding the latter procedure, three methods are implemented for the definition of the theta targets: (i) theta targets are defined by segmenting the latent trait in equal intervals and considering the midpoint of each interval (equal interval procedure, eip), (ii) by clustering the latent trait to obtain unequal intervals and considering the centroids of the clusters as the theta targets (unequal intervals procedure, uip), and (iii) by letting the user set the specific theta targets of interest (user-defined procedure, udp).
        For further details on the procedure, please refer to Epifania, Anselmi & Robusto (2022) <doi:10.1007/978-3-031-27781-8_7>.