Continuous Time Meta-Analysis ('CoTiMA')
This are preset arguments
ctmaAllInvFit
ctmaBiG
ctmaBiGOMX
ctmaCombPRaw
ctmaCompFit
ctmaCorRel
ctmaEmpCov
ctmaEqual
ctmaFit
ctmaFitList
ctmaFitToPrep
ctmaGetPub
ctmaInit
ctmaLabels
ctmaLCS
ctmaOptimizeFit
ctmaOptimizeInit
ctmaPlot
ctmaPlotCtsemMod
ctmaPower
ctmaPRaw
ctmaPrep
ctmaPub
ctmaRedHet
ctmaSaveFile
ctmaScaleInits
ctmaShapeRawData
ctmaStanResample
ctmaStdParams
ctmaSV
plot.CoTiMAFit
summary.CoTiMAFit
The 'CoTiMA' package performs meta-analyses of correlation matrices of repeatedly measured variables taken from studies that used different time intervals. Different time intervals between measurement occasions impose problems for meta-analyses because the effects (e.g. cross-lagged effects) cannot be simply aggregated, for example, by means of common fixed or random effects analysis. However, continuous time math, which is applied in 'CoTiMA', can be used to extrapolate or intrapolate the results from all studies to any desired time lag. By this, effects obtained in studies that used different time intervals can be meta-analyzed. 'CoTiMA' fits models to empirical data using the structural equation model (SEM) package 'ctsem', the effects specified in a SEM are related to parameters that are not directly included in the model (i.e., continuous time parameters; together, they represent the continuous time structural equation model, CTSEM). Statistical model comparisons and significance tests are then performed on the continuous time parameter estimates. 'CoTiMA' also allows analysis of publication bias (Egger's test, PET-PEESE estimates, zcurve analysis etc.) and analysis of statistical power (post hoc power, required sample sizes). See Dormann, C., Guthier, C., & Cortina, J. M. (2019) <doi:10.1177/1094428119847277>. and Guthier, C., Dormann, C., & Voelkle, M. C. (2020) <doi:10.1037/bul0000304>.