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-4.7)mmiCATs_0.2.0.9000.zip(r-4.6)mmiCATs_0.2.0.9000.zip(r-4.5)
mmiCATs_0.2.0.9000.tgz(r-4.6-any)mmiCATs_0.2.0.9000.tgz(r-4.5-any)
mmiCATs_0.2.0.9000.tar.gz(r-4.7-any)mmiCATs_0.2.0.9000.tar.gz(r-4.6-any)
mmiCATs_0.2.0.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
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 from:917d661bfc. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 212 | ||
| source / vignettes | OK | 230 | ||
| linux-release-x86_64 | OK | 218 | ||
| macos-release-arm64 | OK | 117 | ||
| macos-oldrel-arm64 | OK | 105 | ||
| windows-devel | OK | 142 | ||
| windows-release | OK | 129 | ||
| windows-oldrel | OK | 150 | ||
| wasm-release | OK | 148 |
Exports:CloseCATscluster_im_glmRobcluster_im_lmRobKenRCATskenward_rogermmiCATspwr_func_lmer
Dependencies:abindAERbackportsbase64encbdsmatrixbitbit64blobbootbroombroom.mixedbslibcachemcarcarDatacliclusterSEscodacodetoolscollapsecolorspacecommonmarkcowplotcpp11crosstalkDBIDEoptimRDerivdfidxdigestdoBydplyrDTevaluatefarverfastmapfit.modelsfontawesomeforcatsforecastFormulafracdifffsfurrrfuturegenericsggplot2globalsgluegtablehighrhmshtmltoolshtmlwidgetshttpuvisobandjquerylibjsonliteknitrlabelinglaterlatticelazyevallifecyclelistenvlme4lmerTestlmtestlubridatemagrittrMASSMatrixMatrixModelsmaxLikmemoisemgcvmicrobenchmarkmimeminqamiscToolsmlogitmmcardsmodelrmvtnormnlmenloptrnnetnumDerivotelparallellypbkrtestpcaPPpillarpkgconfigplmpoolpromisespurrrquantregR6rappdirsrbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreformulasrlangrmarkdownrobustrobustbaseRPostgresrrcovS7sandwichsassscalesshinyshinythemessourcetoolsSparseMstatmodstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetinytexurcautf8vctrsviridisLitewithrxfunxtableyamlzoo
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 |
