Finite Mixture Models and Meta-Analysis Tools - Based on C.A.MAN
ANOVA for finite mixture models
EM-algorithm for bivariate normally distributed data
EM algorithm and classification for univariate data, for bivariate dat...
VEM algorithm for univariate data, for bivariate data and for meta dat...
Parametric bootstrap
Compute false discovery rates and related statistics
Histograms for finite mixture models
bootstrap replication / validation of finite mixture models
EM algorithm
Fitting Finite Mixture Models
VEM algorithm
Fitting mixture models with covariates
Plot ellipses
Gene calling
VEM algorithm for univariate data, for bivariate data and for meta dat...
Tools for the analysis of finite semiparametric mixtures. These are useful when data is heterogeneous, e.g. in pharmacokinetics or meta-analysis. The NPMLE and VEM algorithms (flexible support size) and EM algorithms (fixed support size) are provided for univariate (Bohning et al., 1992; <doi:10.2307/2532756>) and bivariate data (Schlattmann et al., 2015; <doi:10.1016/j.jclinepi.2014.08.013>).