OSCATS 0.6 Released

The overdue version 0.6 of OSCATS is now available in source and Windows binaries (32- and 64-bit). New 32- and 64-bit Windows binaries of GSL are also available.  While functional, this release should still be considered another in a line of Beta releases leading up to version 1.0.  Testers wanted!  Send feedback to the mailing list or file an issue for coding errors.

Major changes in this release include:

• New OscatsSpace and OscatsPoint classes for a unified representation of continuous, binary, and ordinal latent spaces
• Unification of the OscatsContModel and OscatsDiscrModel into a single OscatsModel based on the new latent space representation
• Generalization of OscatsItem to allow any arbitrary number of models for complex simulation studies

Other highlights include:

• Implementation of the a-Stratified item selection algorithm
• Newly implemented models:
• Partial Credit, Generalized Partial Credit
• Graded Response (Homogenous and Heterogenous Logistic)
• New examples, including implementation of a custom algorithm in Python

For a complete list of changes, see the repository commit log.

Plans for upcoming development include:

• Support for simultaneous testing of multiple examinees
• Exposure control and item selection constraints
• R Bindings
• Support for GObject Introspection
• Example integration with Concerto for CAT administration

Upcoming Developments

After a hiatus, development on OSCATS is continuing.  Below are listed some changes to appear in the Hg repository soon, and the next release is planned for 1-2 months from now.

  Also, there are some new (and rather experimental) introspection-based MATLAB bindings for GObject at the gmatlab project.  They're ready for general use, but those interested who have some stamina may want to take a look.

• GObject-Introspection support for liboscats
• Shift language bindings to GO-I methods (perl, python, java)
• Unified latent space object for continuous and discrete latent variables
• Unified model support, allowing each Administrand to have an arbitrary number of models (for different purposes, e.g. simulation, estimation, calibration, statistical interest)
• Support sub-classing models and algorithms in bound languages
• More common IRT/CD models
• More item selection algorithms, including a-Stratified and Dual-purpose methods
• Simpson-Hetter exposure control