Cosegregation Analysis and Pedigree Simulation
John Michael O. Ranola and Brian H. Shirts
Tools for generating and analyzing pedigrees.  Specifically, this has functions that will generate realistic pedigrees for the USA and China based on historical birth rates and family sizes.  It also has functions for analyzing these pedigrees when they include disease information including one based on counting meioses and another based on likelihood ratios.

c(person("Christian", "Dormann", role = c("aut","cph")),
person("Markus", "Homberg", email = "cotima@uni-mainz.de", role = c("ctb", "com", "cre")),
person("Christina", "Guthier", role = c("ctb")),
person("Manuel", "Voelkle", role = c("ctb")))
Christian Dormann [aut, cph],
Markus Homberg [ctb, com, cre],
Christina Guthier [ctb],
Manuel Voelkle [ctb]

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>.