Fits the lifespan datasets of biological systems such as yeast, fruit flies, and other similar biological units with well-known finite mixture models introduced by Farewell V. (1982) <doi:10.2307/2529885> and Al-Hussaini et al. (2000) <doi:10.1080/00949650008812033>. Estimates parameter space fitting of a lifespan dataset with finite mixtures of parametric distributions. Computes the following tasks; 1) Estimates parameter space of the finite mixture model 
             by implementing the expectation maximization (EM) algorithm. 2) Finds a sequence of four goodness-of-fit measures consist of Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Kolmogorov-Smirnov (KS), and log-likelihood (log-likelihood) statistics. 3)The initial values is determined by k-means clustering.

The fit.models function and its associated methods (coefficients, print,
  summary, plot, etc.) were originally provided in the robust package to compare robustly
  and classically fitted model objects. See chapters 2, 3, and 5 in Insightful (2002)
  'Robust Library User's Guide' <http://robust.r-forge.r-project.org/Robust.pdf>). The aim
  of the fit.models package is to separate this fitted model object comparison functionality
  from the robust package and to extend it to support fitting methods (e.g., classical,
  robust, Bayesian, regularized, etc.) more generally.

Contains Probability Mass Functions, Cumulative Mass
    Functions, Negative Log Likelihood value, parameter estimation and
    modeling data using Binomial Mixture Distributions (BMD) (Manoj et al
    (2013) <doi:10.5539/ijsp.v2n2p24>) and Alternate Binomial
    Distributions (ABD) (Paul (1985) <doi:10.1080/03610928508828990>),
    also Journal article to use the package(<doi:10.21105/joss.01505>).

For binomial outcome data Alternate Binomial Distributions
    and Binomial Mixture Distributions are fitted when overdispersion is
    available.

Fits Weibull or sigmoidal models to percent loss conductivity (plc) curves as a function of plant water potential, computes confidence intervals of parameter estimates and predictions with bootstrap or parametric methods, and provides convenient plotting methods.