## 3 Results
### Working Examples: Fitting Finite Mixture Models to Lifespan Datasets
For example, a similar model of a mixture of a Weibull component and a surviving fraction in the context of a lung cancer trial is considered [36; 37; 38] and finite mixture distributions are broadly applied in medical and survival statistics (for numerous examples, see [18; 39]). The **fitmix** package is open-source software released under a GPL3 license on CRAN. As future work, we think of some software functionality ideas that include: (1) an expanded set of the finite mixture models, particularly the binomial aging model outlined in [PERSON] et al. (2019) and [PERSON] et al. (2016) [9; 40]; (2) a function that automates plots and illustrates survival datasets fitted by mixture models; (3) expansion of user-detected goodness-of-fit measures, including a comparison to the best-fit model; and (4) addition of a Gaussian noise effect [14] that accommodates grouped distribution and replicative lifespan data of the budding yeast, e.g., stands, wildtype strains, single-gene mutant deleted genes. Another study that could be conducted is examining distinct mixture models to model heterogeneous lifetime data, particularly RLS data. A mixture of Gompertz distributions, a mixture of Log-logistic distributions, a mixture of Log-normal distributions, and a mixture of Weibull distributions would be tested for the best fit to the experimental and simulated survival datasets. The comparisons of theoretical statistical distributions and mixture distribution models for lifetime and survival datasets would be studied in the future. The following are available online at [[https://github.com/guven-code/AppliedScience_fitmix](https://github.com/guven-code/AppliedScience_fitmix)]([https://github.com/guven-code/AppliedScience_fitmix](https://github.com/guven-code/AppliedScience_fitmix)) and [[https://www.mdpi.com/article/10.3390/app1136114/s1](https://www.mdpi.com/article/10.3390/app1136114/s1)]([https://www.mdpi.com/article/10.3390/app1136114/s1](https://www.mdpi.com/article/10.3390/app1136114/s1)). Principal Author, E.G. ; Conceptualization, E.G. and H.Q. ; methodology, E.G. and H.Q. ; software, E.G. ; validation, E.G. and H.Q. ; formal analysis, E.G. and H.Q. ; investigation, E.G. and H.Q. ; resources, E.G. and H.Q. ; data curation, E.G and H.Q. ; writing--original draft preparation, E.G. ; writing--review and editing, E.G. and H.Q. ; visualization, E.G. ; supervision, H.Q. ; project administration, E.G. and H.Q. ; funding acquisition, H.Q. All authors have read and agreed to the published version of the manuscript. HQ thanks the support of NSF Career award #1453078 and #1720215, NSF BD Spoke #1761839. **Institutional Review Board Statement**: Not applicable. **Data Availability Statement**: The data that support the findings of this study are available through personal communication with [PERSON] via email: [EMAIL_ADDRESS]. The authors declare that they have no conflict of interest. ## References

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