## 1 Introduction  
An increase in mortality rate is typically interpreted as aging. In cellular aging, the budding yeast _S. cerevisiae_ has revealed many significant properties in the mechanism of eukaryotic lifespan regulation [1, 2, 3]. Studies of yeast aging have led to many conserved components that affect lifespan [2, 4, 5]. Yeast lifespan is typically measured by two different approaches. The first is chronological lifespan (CLS), defined as the duration of time that a mother cell remains alive without division. The second is the replicative lifespan (RLS) of a mother cell, calculated as the number of mother cell divisions until senescence [6]. Historically, analysis of the RLS data has been conducted with nonparametric methods or using typical parametric survival models [7]. Survival analysis of the lifespan datasets has been generally delineated with the standard lifespan statistical distributions such as Gompertz, Weibull, Logistic, and Log-logistic [8]. In addition to these conventional statistical distribution models, several models for RLS data of budding yeast have been recently published [9]. Recent studies show that yeast replicative aging is heterogeneous and contains at least two subpopulations [10, 11], which argues for mixture models. Finite mixtures are studied and applied to several applications, especially in biological (failure data) and medical (disease distributions) areas since the end of the 19 th century. Both grouped and ungrouped datasets are aimed at the calculation of the parameters of finite mixtures [12]. Heterogeneous distributions of mixture population models such as the budding yeast _S. cerevisiae_ can be utilized to model a population with subpopulations. Insights might be gained by comparing the mixture models of the wildtypes, mutants, and various treatments such as calorie restriction. The mixture survival analysis of the RLS of a cell is a compilation of statistical approaches for lifespan data analysis of mixture models expressing heterogeneous states of yeast cell populations with diverse genotypes. In a complete population, mixture components are the densities (probabilities) of the subpopulations, and weights are represented by the fraction of each subpopulation in the complete population. Frequency distribution is useful in the instance of single-mode survival lifespan data with the conventional probability distribution model [9; 13; 14]. The mixture of probability density functions is even more advantageous since it is used to portray a heterogeneous lifetime dataset when there is an indication of simple bimodality or multimodality [15; 16]. The RLSs of the budding yeast distributions are not sufficiently portrayed by a single probability distribution since mother cell decrease is related to asymmetric phenotypes such as dysregulation of vacuole acidity, genomic instability, and partial reservation of protein aggregates [3; 6; 17]. In such settings, often finite mixture distributions are used to describe complex and multi-modal asymmetric division distributions. [PERSON] et al. (2005) propose a mixture of Weibull models utilizing Bayesian analysis to model the heterogeneous lifespans [18], generalizing an earlier finding that [PERSON] (2002) studied Weibull distributions of a finite mixture with a fixed number of components [19]. In [PERSON] et al. (1999), a finite mixture model of a Gompertz component and a surviving portion in the framework of Types I and II censored samples from heterogeneous population is considered [20]. The basic goal of the functions of **fitmix**, which is an open-source R package, is to offer a confined set of models to fit a diverse lifespan, even with large datasets, specifically the replicative lifespan of budding yeast or other biological units.