| Summary: | Replicative lifespan (RLS) of the budding yeast is the number of mother cell divisions until senescence and is instrumental to understanding mechanisms of cellular aging. Recent research has shown that replicative aging is heterogeneous, which argues for mixture modeling. The mixture model is a statistical method to infer subpopulations of the heterogeneous population. Mixture modeling is a relatively underdeveloped area in the study of cellular aging. There is no open access software currently available that assists extensive comparison among mixture modeling methods. To address these needs, we developed an R package called <b>fitmix</b> that facilitates the computation of well-known distributions utilized for RLS data and other lifetime datasets. This package can generate a group of functions for the estimation of probability distributions and simulation of random observations from well-known finite mixture models including Gompertz, Log-logistic, Log-normal, and Weibull models. To estimate and compute the maximum likelihood estimates of the model parameters, the Expectation–Maximization (EM) algorithm is employed.
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