mmiCATs: Cluster Adjusted t Statistic Applications
Simulation results detailed in Esarey and Menger (2019) <doi:10.1017/psrm.2017.42>
demonstrate that cluster adjusted t statistics (CATs) are an effective method
for correcting standard errors in scenarios with a small number of clusters.
The 'mmiCATs' package offers a suite of tools for working with CATs. The
mmiCATs() function initiates a 'shiny' web application, facilitating
the analysis of data utilizing CATs, as implemented in the cluster.im.glm()
function from the 'clusterSEs' package. Additionally, the pwr_func_lmer()
function is designed to simplify the process of conducting simulations to
compare mixed effects models with CATs models. For educational purposes, the
CloseCATs() function launches a 'shiny' application card game, aimed at enhancing
users' understanding of the conditions under which CATs should be preferred
over random intercept models.
| Version: |
0.1.1 |
| Imports: |
broom, broom.mixed, clusterSEs, DT, lmerTest, MASS, mmcards, robust, robustbase, shiny, shinythemes |
| Suggests: |
testthat (≥ 3.0.0) |
| Published: |
2024-02-21 |
| Author: |
Mackson Ncube [aut, cre],
mightymetrika, LLC [cph, fnd] |
| Maintainer: |
Mackson Ncube <macksonncube.stats at gmail.com> |
| BugReports: |
https://github.com/mightymetrika/mmiCATs/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://github.com/mightymetrika/mmiCATs |
| NeedsCompilation: |
no |
| Materials: |
README |
| CRAN checks: |
mmiCATs results |
Documentation:
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