Description: 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>.

person("Markus", "Homberg", email = "cotima@uni-mainz.de", role = c("aut", "com", "cre")),

scholar (>= 0.2.0), stringi (>= 1.0.7), MASS, methods

Title: Genomic Prediction Using SNP Codata
Description: Computes genomic breeding values using external information on the markers. The package fits a linear mixed model with heteroscedastic random effects, where the random effect variance is fitted using a linear predictor and a log link. The method is described in Mouresan, Selle and Ronnegard (2019) <doi:10.1101/636746>.

Title: Analysis of R Code for Reproducible Research and Code
Description: Tools for analyzing R expressions
or blocks of code and determining the dependencies between them.
It focuses on R scripts, but can be used on the bodies of functions.
There are many facilities including the ability to summarize  or get a high-level
view of code, determining dependencies between variables,  code improvement
Author: Duncan Temple Lang, Roger Peng, Deborah Nolan, Gabriel Becker
Suggests: graph, Rgraphviz, RUnit, knitr, highlight, RJSONIO, RCurl
Collate: classes.R librarySymbols.R functionHandlers.R codeDepends.R
sectionDepends.R sweave.R xml.R jss.R frags.R codeTypes.R gc.R
graph.R parallel.R deps.R separateBlocks.R callGraph.R isPlot.R