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 et al. (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.

Tools for fitting and analyzing 1D-4D nuclear magnetic resonance
    spectra with analytical models of peak shapes and peak groups. The
    package reads spectra in 'NMRPipe' format, builds constrained
    parameter structures for chemical shifts, line widths, scalar
    couplings, volumes, and phases, and performs nonlinear
    least-squares optimization for iterative peak discovery or
    simultaneous fits across multiple spectra. It also provides methods
    for visualization, preprocessing, and kinetic analysis of 1D
    time-series data, including automated phase optimization, solvent
    suppression, time-domain correction for frequency shifts and line
    broadening, modeling spectra as linear combinations of two
    component spectra, and exponential rate fitting.

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.

Provides functions for plotting probability density functions, distribution functions, survival functions, hazard functions and computing distribution moments. The implementation is inspired by Delignette-Muller and Dutang (2015) <doi:10.18637/jss.v064.i04>.