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.