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Stochastic Process Model has many applications in analysis of longitudinalbiodemographic data. Such data contain various physiological variables(sometimes known as covariates). It also can potentially contain geneticinformation available for all or a part of participants. Taking advantage fromboth genetic and non-genetic information can provide future insights into abroad range of processes describing aging-related changes in the organism. Inthis paper, we implemented a multi-dimensional Genetic Stochastic Process Model(GenSPM) in newly developed software tool, R-package stpm, which allowsresearchers performing such kind of analysis.

Multidimensional Stochastic Process Model and its Applications to Analysis of Longitudinal Data with Genetic Information