c(person("Marie-Laure", "Delignette-Muller", role = "aut", email = "marielaure.delignettemuller@vetagro-sup.fr"),
person("Christophe", "Dutang", role = "aut", email = "christophe.dutang@ensimag.fr"),
person("Regis", "Pouillot", role = "ctb"),
person("Jean-Baptiste", "Denis", role = "ctb"),
person("Aurelie", "Siberchicot", role = c("aut", "cre"), email = "aurelie.siberchicot@univ-lyon1.fr"))

Extends the fitdistr() function (of the MASS package) with several functions
to help the fit of a parametric distribution to non-censored or censored data.
Censored data may contain left censored, right censored and interval censored values,
with several lower and upper bounds. In addition to maximum likelihood estimation (MLE),
the package provides moment matching (MME), quantile matching (QME), maximum goodness-of-fit
estimation (MGE) and maximum spacing estimation (MSE) methods (available only for
non-censored data). Weighted versions of MLE, MME, QME and MSE are available. See e.g.
Casella & Berger (2002), Statistical inference, Pacific Grove, for a general introduction
to parametric estimation.
actuar, rgenoud, mc2d, gamlss.dist, knitr, ggplot2,
GeneralizedHyperbolic, rmarkdown, Hmisc

R (>= 3.5.0), MASS, grDevices, survival, methods

Finite Mixture Model Fitting of Lifespan Datasets
c(person("Emine", "Guven", email = "emine.guven33@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0001-9324-0879")),
person("Hong", "Qin", role = c("aut")))
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.
Emine Guven [aut, cre] (<https://orcid.org/0000-0001-9324-0879>),
Hong Qin [aut]