4 packages on CRAN
Provides functions to simulate data from large-scale educational assessments, including background questionnaire data and cognitive item responses that adhere to a multiple-matrix sampled design.
Generates binary test data based on Item Response Theory using the two-parameter logistic model (Lord, 1980 <doi:10.4324/9780203056615>). Useful functions for test equating are included, e.g. functions for generating internal and external common items between test forms and a function to create a linkage plans between those forms. Ancillary functions for generating true item and person parameters as well as for calculating the probability of a person correctly answering an item are also included.
Bayesian seemingly unrelated regression with general variable selection and dense/sparse covariance matrix. The sparse seemingly unrelated regression is described in Banterle et al. (2018) <doi:10.1101/467019>.
Enables users to handle the dataset cleaning for conducting specific analyses with the log files from two international educational assessments: the Programme for International Student Assessment (PISA, <http://www.oecd.org/pisa/>) and the Programme for the International Assessment of Adult Competencies (PIAAC, <http://www.oecd.org/skills/piaac/>). An illustration of the analyses can be found on the LOGAN Shiny app (<https://loganpackage.shinyapps.io/shiny/>) on your browser.