Package: powerly 1.8.6

powerly: Sample Size Analysis for Psychological Networks and More

An implementation of the sample size computation method for network models proposed by Constantin et al. (2021) <doi:10.31234/osf.io/j5v7u>. The implementation takes the form of a three-step recursive algorithm designed to find an optimal sample size given a model specification and a performance measure of interest. It starts with a Monte Carlo simulation step for computing the performance measure and a statistic at various sample sizes selected from an initial sample size range. It continues with a monotone curve-fitting step for interpolating the statistic across the entire sample size range. The final step employs stratified bootstrapping to quantify the uncertainty around the fitted curve.

Authors:Mihai Constantin [aut, cre]

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NEWS

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

Peer review:

Bug tracker:https://github.com/mihaiconstantin/powerly/issues

On CRAN:

network-modelspower-analysispsychologysample-size-calculation

3.54 score 7 stars 2 scripts 302 downloads 3 exports 153 dependencies

Last updated 2 years agofrom:13d86e13df. Checks:OK: 5 NOTE: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 02 2024
R-4.5-winNOTENov 02 2024
R-4.5-linuxNOTENov 02 2024
R-4.4-winOKNov 02 2024
R-4.4-macOKNov 02 2024
R-4.3-winOKNov 02 2024
R-4.3-macOKNov 02 2024

Exports:generate_modelpowerlyvalidate

Dependencies:abindbackportsbase64encbitbit64bootbootnetbroombslibcachemcheckmateclassclicliprclustercocorcodetoolscolorspacecorpcorcorrplotcpp11crayondata.tabledigestdoParalleldplyre1071eigenmodelellipseevaluatefansifarverfastmapfdrtoolfontawesomeforcatsforeachforeignFormulafsgdatagenericsggplot2glassoglmnetglueGPArotationgridExtragtablegtoolshavenhighrHmischmshtmlTablehtmltoolshtmlwidgetsigraphIsingFitIsingSamplerisobanditeratorsjomojpegjquerylibjsonliteknitrlabelinglatticelavaanlifecyclelme4magrittrMASSMatrixmemoisemgcvmgmmicemimeminqamitmlmnormtmunsellmvtnormNetworkToolboxnetworktoolsnlmenloptrnnetnnlsnumDerivordinalpanpatchworkpbapplypbivnormpillarpkgconfigplotrixplyrpngpolynomppcorprettyunitsprogressproxypsychpurrrpwrqgraphquadprogR.matlabR.methodsS3R.ooR.utilsR6rappdirsRColorBrewerRcppRcppArmadilloRcppEigenreadrreshape2rlangrmarkdownrpartrstudioapisassscalesshapesmacofsnowsplines2stringistringrsurvivaltibbletidyrtidyselecttinytextzdbucminfutf8vctrsviridisviridisLitevroomweightswithrwordcloudxfunyaml