Package: meow 1.0.0

meow: Unified Framework for Computer Adaptive Testing Simulations

Provides an extensible framework for conducting simulations to compare data generating processes, item selection algorithms, parameter update algorithms, and stopping rules in computer adaptive testing (CAT) applications. Bundled algorithms include the Elo-based update rules of Klinkenberg, Straatemeier and van der Maas (2011) <doi:10.1016/j.compedu.2011.02.003> and Vermeiren, Kruis, Bolsinova, van der Maas and Hofman (2025) <doi:10.1016/j.caeai.2025.100376>.

Authors:Klint Kanopka [aut, cre], Sophia Deng [aut]

meow_1.0.0.tar.gz
meow_1.0.0.zip(r-4.7)meow_1.0.0.zip(r-4.6)meow_1.0.0.zip(r-4.5)
meow_1.0.0.tgz(r-4.6-any)meow_1.0.0.tgz(r-4.5-any)
meow_1.0.0.tar.gz(r-4.7-any)meow_1.0.0.tar.gz(r-4.6-any)
meow_1.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
meow/json (API)

# Install 'meow' in R:
install.packages('meow', repos = c('https://klintkanopka.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/klintkanopka/meow/issues

Pkgdown/docs site:https://klintkanopka.com

On CRAN:

Conda:

5.74 score 17 scripts 20 exports 5 dependencies

Last updated from:6bcc995f5d. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK129
source / vignettesOK189
linux-release-x86_64OK128
macos-release-arm64OK94
macos-oldrel-arm64OK99
windows-develOK81
windows-releaseOK88
windows-oldrelOK88
wasm-releaseOK141

Exports:construct_adj_matdata_existingdata_simple_1pledge_weight_exponentialedge_weight_inverseedge_weight_linearedge_weight_negative_logedge_weight_powermeowmeow_administeredmeow_longselect_max_distselect_max_dist_enhancedselect_max_infoselect_randomselect_restrict_rateselect_sequentialupdate_maths_gardenupdate_prowise_learnupdate_theta_mle

Dependencies:RcppRcppArmadilloRcppParallelRfastzigg

The meow Workflow: Visualizing Exposure Control Methods
No exposure controls | RMSE of person abilities | Restricting item exposure | Visualizing the adjacency matrix
Rendered fromintro-viz.Rmdusingknitr::rmarkdown

Last update: 2026-06-29
Started: 2026-06-29

Data Loaders
What a data loader returns | The resp object | The pers_tru object | The item_tru object | Function arguments | The bundled 1PL loader | A note on random seeds
Rendered fromdata-loaders.Rmdusingknitr::rmarkdown

Last update: 2026-06-29
Started: 2026-06-28

Extending meow: Writing Your Own Modules
The simulation state | Data loaders | Item selection functions | Parameter update functions | Putting it together | Checklist
Rendered fromextending-meow.Rmdusingknitr::rmarkdown

Last update: 2026-06-29
Started: 2026-06-28

Item Selection Functions
Function signature | Bundled selectors | Sequential | Random | Maximum information | Network distance | Writing a custom selector | Best practices
Rendered fromitem-selection.Rmdusingknitr::rmarkdown

Last update: 2026-06-29
Started: 2026-06-28

Parameter Update Functions
Function signature | Bundled updaters | Maximum likelihood ability estimation | Elo-style updates (Maths Garden) | Paired Elo updates (Prowise Learn) | Best practices
Rendered fromparameter-update.Rmdusingknitr::rmarkdown

Last update: 2026-06-29
Started: 2026-06-28

Implementing the Maths Garden Update Algorithm
Mathematical foundation | Implementation in meow | Using it | Extending the algorithm | Practical notes
Rendered frommaths-garden-update.Rmdusingknitr::rmarkdown

Last update: 2026-06-28
Started: 2026-06-28

Network-Based Item Selection
Mathematical foundation | Edge weight strategies | Implementation | Using different edge weight strategies | Choosing a strategy | Considerations
Rendered fromnetwork-item-selection.Rmdusingknitr::rmarkdown

Last update: 2026-06-28
Started: 2026-06-28

Implementing the Prowise Learn Update Algorithm
Mathematical foundation | Implementation in meow | Using it | Practical notes
Rendered fromprowise-learn-update.Rmdusingknitr::rmarkdown

Last update: 2026-06-28
Started: 2026-06-28