Block-diagonal covariance selection for high dimensional Gaussian
    graphical models. The selection procedure is based on the slope heuristics.

Collect  your data on digital marketing campaigns from 'Shopify' Ads using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.

An interface to the Admin API of the E-commerce service Shopify,
    (<https://help.shopify.com/en/api/reference>).

Provides tools for shoreline dating coastal Stone Age sites. The 
  implemented method was developed in Roalkvam (2023) 
  <doi:10.1016/j.quascirev.2022.107880> for the Norwegian Skagerrak coast. 
  Although it can be extended to other areas, this also forms the core area for 
  application of the package. Shoreline dating is based on the present-day 
  elevation of a site, a reconstruction of past relative sea-level change, and
  empirically derived estimates of the likely elevation of the sites above the 
  contemporaneous sea-level when they were in use. The geographical and temporal 
  coverage of the method thus follows from the availability of local geological 
  reconstructions of shoreline displacement and the degree to which the 
  settlements to be dated have been located on or close to the shoreline when 
  they were in use. Methods for numerical treatment and visualisation of the 
  dates are provided, along with basic tools for visualising and evaluating the 
  location of sites.

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.