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meow 1.0.0

First public release.

Features

• meow() runs a full CAT administration simulation from three swappable modules: a data loader, an item selection function, and a parameter update function.
• The simulation state is matrix-based for speed. Item selection and parameter update functions receive a respondent-by-item response matrix R and an integer administration matrix admin; person and item parameters are kept as data frames so users can add arbitrary parameters.
  • Item selection functions take (pers, item, R, admin, adj_mat, ...) and return an updated admin matrix with newly administered cells marked non-zero.
  • Parameter update functions take (pers, item, R, admin, ...) and return a list with updated pers and item data frames.
• Bundled data loaders (data_existing(), data_simple_1pl()), item selectors (select_sequential(), select_random(), select_max_info(), select_restrict_rate(), select_max_dist(), select_max_dist_enhanced()), and parameter updaters (update_theta_mle(), update_maths_garden(), update_prowise_learn()).
• Helpers for module authors: meow_long() converts the matrix state to a long (id, item, resp) data frame, meow_administered() returns a logical mask of administered items, and construct_adj_mat() builds the item co-exposure matrix.
• meow() accepts a keep_adj_mats argument; set it to FALSE to retain only the final adjacency matrix and save memory on large or long simulations.
• Vignettes cover getting started, each module type, the bundled algorithms, and a dedicated “Extending meow” guide to writing your own modules.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.