Package: mmiCATs 0.2.0.9000
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.
Authors:
mmiCATs_0.2.0.9000.tar.gz
mmiCATs_0.2.0.9000.zip(r-4.5)mmiCATs_0.2.0.9000.zip(r-4.4)mmiCATs_0.2.0.9000.zip(r-4.3)
mmiCATs_0.2.0.9000.tgz(r-4.4-any)mmiCATs_0.2.0.9000.tgz(r-4.3-any)
mmiCATs_0.2.0.9000.tar.gz(r-4.5-noble)mmiCATs_0.2.0.9000.tar.gz(r-4.4-noble)
mmiCATs_0.2.0.9000.tgz(r-4.4-emscripten)mmiCATs_0.2.0.9000.tgz(r-4.3-emscripten)
mmiCATs.pdf |mmiCATs.html✨
mmiCATs/json (API)
| # Install 'mmiCATs' in R: |
| install.packages('mmiCATs', repos = c('https://mightymetrika.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/mightymetrika/mmicats/issues
Last updated 3 months agofrom:917d661bfc. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 02 2024 |
| R-4.5-win | OK | Nov 02 2024 |
| R-4.5-linux | OK | Nov 02 2024 |
| R-4.4-win | OK | Nov 02 2024 |
| R-4.4-mac | OK | Nov 02 2024 |
| R-4.3-win | OK | Nov 02 2024 |
| R-4.3-mac | OK | Nov 02 2024 |
Exports:CloseCATscluster_im_glmRobcluster_im_lmRobKenRCATskenward_rogermmiCATspwr_func_lmer
Dependencies:abindAERbackportsbase64encbdsmatrixbitbit64blobbootbroombroom.mixedbslibcachemcarcarDatacliclusterSEscodacodetoolscollapsecolorspacecommonmarkcowplotcpp11crayoncrosstalkDBIDEoptimRDerivdfidxdigestdoBydplyrDTevaluatefansifarverfastmapfit.modelsfontawesomeforcatsFormulafsfurrrfuturegenericsggplot2globalsgluegtablehighrhmshtmltoolshtmlwidgetshttpuvisobandjquerylibjsonliteknitrlabelinglaterlatticelazyevallifecyclelistenvlme4lmerTestlmtestlubridatemagrittrMASSMatrixMatrixModelsmaxLikmemoisemgcvmicrobenchmarkmimeminqamiscToolsmlogitmmcardsmodelrmunsellmvtnormnlmenloptrnnetnumDerivparallellypbkrtestpcaPPpillarpkgconfigplmplogrpoolpromisespurrrquantregR6rappdirsrbibutilsRColorBrewerRcppRcppEigenRdpackrlangrmarkdownrobustrobustbaseRPostgresrrcovsandwichsassscalesshinyshinythemessourcetoolsSparseMstatmodstringistringrsurvivaltibbletidyrtidyselecttimechangetinytexutf8vctrsviridisLitewithrxfunxtableyamlzoo
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| CloseCATs Shiny Application | CloseCATs |
| Cluster-Adjusted Confidence Intervals And p-Values Robust GLMs | cluster_im_glmRob |
| Cluster-Adjusted Confidence Intervals And p-Values Robust Linear Models | cluster_im_lmRob |
| Launch KenRCATs Shiny Application | KenRCATs |
| Kenward-Roger Analysis Shiny Application | kenward_roger |
| Set Up CATs Analysis in Shiny Application | mmiCATs |
| Power Analysis for Clustered Data | pwr_func_lmer |
