igraph, rgl, ellipse, corpcor, RColorBrewer, plyr,parallel,
dplyr, tidyr, reshape2, methods, matrixStats, rARPACK,
gridExtra

igraph, rgl, ellipse, corpcor, RColorBrewer, parallel, dplyr,
tidyr, reshape2, methods, matrixStats, rARPACK, gridExtra

This package fits multivariate mixtures of various parametric proportional hazard models using the EM-Algorithm. Proportionality restrictions can be imposed on the latent groups and/or on the variables. Several survival distributions can be specified. Missing values are allowed. Independence is assumed over the single variables.

This package fits multiple variable mixtures of various parametric proportional hazard models using the EM-Algorithm. Proportionality restrictions can be imposed on the latent groups and/or on the variables. Several survival distributions can be specified. Missing values are allowed. Independence is assumed over the single variables.

c(person(given="Patrick", family="Mair", email="mair@fas.harvard.edu", role=c("cre","aut")),
person(given="Marcus", family="Hudec", role="aut"))
This package fits multiple variable mixtures of various parametric proportional hazard models using the EM-Algorithm. Proportionality restrictions can be imposed on the latent groups and/or on the variables. Several survival distributions can be specified. Missing values and censored values are allowed. Independence is assumed over the single variables.
Patrick Mair [cre, aut],
Marcus Hudec [aut]

Fits multiple variable mixtures of various parametric proportional hazard models using the EM-Algorithm. Proportionality restrictions can be imposed on the latent groups and/or on the variables. Several survival distributions can be specified. Missing values and censored values are allowed. Independence is assumed over the single variables.

Finite Mixture Modeling for Raw and Binned Data
person("Youjiao", "Yu", email = "jiaoisjiao@gmail.com",
role = c("aut", "cre"))
Performs maximum likelihood estimation for finite mixture models for families including Normal, Weibull, Gamma and Lognormal by using EM algorithm, together with Newton-Raphson algorithm or bisection method when necessary. It also conducts mixture model selection by using information criteria or bootstrap likelihood ratio test. The data used for mixture model fitting can be raw data or binned data. The model fitting process is accelerated by using R package 'Rcpp'.
ggplot2 (>= 2.1.0), graphics, Rcpp (>= 0.12.8), stats