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: 0.2.0 Imports: broom, broom.mixed, clusterSEs, DT, lmerTest, MASS, mmcards, pool, robust, robustbase, RPostgres, shiny, shinythemes Suggests: testthat (≥ 3.0.0) Published: 2024-08-26 DOI: 10.32614/CRAN.package.mmiCATs 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: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=mmiCATs to link to this page.
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4