methods, stats, utils, graphics, grDevices

Random univariate and multivariate finite mixture model generation, estimation, clustering, latent class analysis and classification. Variables can be continuous, discrete, independent or dependent and may follow normal, lognormal, Weibull, gamma, Gumbel, binomial, Poisson, Dirac, uniform or circular von Mises parametric families.

R functions for random univariate and multivariate finite
mixture generation, number of components, component weights and
component parameters estimation and plotting of finite
mixtures.

R functions for random univariate and multivariate finite
mixture generation, number of components, component weights and
component parameters estimation, printing and plotting of
finite mixtures and class membership prediction. Variables can
be either continuous or discrete.

R functions for random univariate and multivariate finite
mixture generation, number of components, component weights and
component parameters estimation, printing and plotting of
finite mixtures, bootstrapping and class membership prediction.
Variables can be either continuous or discrete, may follow
normal, lognormal, Weibull, binomial, Poisson or Dirac
parametric families and should be independent within
components.

R functions for random univariate and multivariate finite
mixture generation, number of components, component weights and
component parameters estimation, printing and plotting of
finite mixtures, bootstrapping and class membership prediction.
Variables can be either continuous or discrete, may follow
normal, lognormal, Weibull, gamma, binomial, Poisson or Dirac
parametric families and should be independent within
components.

R functions for random univariate and multivariate finite mixture generation, number of components, component weights and component parameters estimation, printing and plotting of finite mixtures, bootstrapping and class membership prediction. Variables can be either continuous or discrete, may follow normal, lognormal, Weibull, gamma, binomial, Poisson or Dirac parametric families and should be independent within components.