DAKS: An R package for data analysis methods in knowledge space theory. Manuscript under revision

Knowledge space theory is part of psychometrics and provides a theoretical framework for the modeling, assessment, and training of knowledge. It utilizes the idea that some pieces of knowledge may imply others, and is based on order and set theory. We introduce the R package DAKS for performing basic and complex operations in knowledge space theory. This package implements three inductive item tree analysis algorithms for deriving quasi orders from binary data, the original, corrected, and minimized corrected algorithms. It provides functions for computing population and estimated asymptotic variances of the diff fit measures, and for switching between test item and knowledge state representations. Other features are a Hasse diagram drawing device, a data simulation tool based on a finite mixture latent variable model, and a function for computing response pattern and knowledge state frequencies. We describe the functions of the package and demonstrate their usage by real and simulated data examples