Changes in version 1.0.0 (2026-07-06)                  

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.