A consistent, unified and extensible
    framework for estimation of parameters for probability distributions,
    including parameter estimation procedures that allow for weighted samples;
    the current set of distributions included are: the standard beta,
    The four-parameter beta, Burr, gamma, Gumbel, Johnson SB and SU, Laplace,
    logistic, normal, symmetric truncated normal, truncated normal,
    symmetric-reflected truncated beta, standard symmetric-reflected truncated
    beta, triangular, uniform, and Weibull distributions; decision criteria
    and selections based on these decision criteria.

Survival analysis is employed to model time-to-event data. This package examines the relationship between survival and one or more predictors, termed as covariates, which can include both treatment variables (e.g., season of birth, represented by indicator functions) and continuous variables. To this end, the Cox-proportional hazard (Cox-PH) model, introduced by Cox in 1972, is a widely applicable and commonly used method for survival analysis. This package enables the estimation of the effect of randomization for the treatment variable to account for potential confounders, providing adjustment when estimating the association with exposure. It accommodates both fixed and time-dependent covariates and computes survival probabilities for lactation periods in dairy animals. The package is built upon the algorithm developed by Klein and Moeschberger (2003) <DOI:10.1007/b97377>.

Creates family objects identical to stats
    family but for new links.

Provides computational tools for working with the Extended Laplace distribution, including the probability density function, cumulative distribution function, quantile function, random variate generation based on convolution with Uniform noise and the quantile-quantile plot. Useful for modeling contaminated Laplace data and other applications in robust statistics. See Saah and Kozubowski (2025) <doi:10.1016/j.cam.2025.116588>.