Simulated Example Datasets From Baudry et al. (2010)
Resampling-based Inference for Gaussian finite mixture models
Dimension reduction for model-based clustering and classification
BIC for Parameterized Gaussian Mixture Models
Component Density for Parameterized MVN Mixture Models
Component Density for a Parameterized MVN Mixture Model
Cumulative Distribution and Quantiles for a univariate Gaussian mixture distribution
Pairwise Scatter Plots showing Classification
Combining Gaussian Mixture Components for Clustering
Plot Classifications Corresponding to Successive Combined Solutions
Coordinate projections of multidimensional data modeled by an MVN mixture.
Weighted means, covariance and scattering matrices conditioning on a weighted matrix.
Convert mixture component covariances to matrix form.
Default conjugate prior for Gaussian mixtures.
Density Estimation via Model-Based Clustering
Diagnostic plots for 'mclustDensity' estimation
EM algorithm starting with E-step for parameterized Gaussian mixture models.
Set control values for use with the EM algorithm.
EM algorithm starting with E-step for a parameterized Gaussian mixture model.
E-step for parameterized Gaussian mixture models.
E-step in the EM algorithm for a parameterized Gaussian mixture model.
Aproximate Hypervolume for Multivariate Data
ICL for an estimated Gaussian Mixture Model
Missing Data Imputation via the 'mix' package
Pairwise Scatter Plots showing Missing Data Imputations
Normal Mixture Modeling for Model-Based Clustering, Classification, and Density Estimation
Default values for use with MCLUST package
Plot one-dimensional data modeled by an MVN mixture.
Plot two-dimensional data modelled by an MVN mixture.
Bootstrap Likelihood Ratio Test for the Number of Mixture Components
ICL Criterion for Model-Based Clustering
Template for variance specification for parameterized Gaussian mixture models
EM algorithm starting with M-step for parameterized MVN mixture models.
EM algorithm with weights starting with M-step for parameterized MVN mixture models
EM algorithm starting with M-step for a parameterized Gaussian mixture model.
M-step for parameterized Gaussian mixture models.
M-step for a parameterized Gaussian mixture model.
Number of Estimated Parameters in Gaussian Mixture Models
Number of Variance Parameters in Gaussian Mixture Models
Classifies Data According to Unique Observations
Plot of bootstrap distributions for mixture model parameters
Plotting method for MclustDA discriminant analysis
Plotting method for dimension reduction for model-based clustering and classification
Plots for Mixture-Based Density Estimate
Cluster multivariate observations by Gaussian finite mixture modeling
Classify multivariate observations by Gaussian finite mixture modeling
Classify multivariate observations on a dimension reduced subspace by Gaussian finite mixture modeling
Density estimate of multivariate observations by Gaussian finite mixture modeling
Random projections of multidimensional data modeled by an MVN mixture.
Convert mixture component covariances to decomposition form.
Simulate from Parameterized MVN Mixture Models
Simulate from a Parameterized MVN Mixture Model
Summarizing Gaussian Finite Mixture Model Fits
Summary Function for Bootstrap Inference for Gaussian Finite Mixture Models
Summarizing discriminant analysis based on Gaussian finite mixture modeling.
Summarizing dimension reduction method for model-based clustering and classification
Summary function for model-based clustering via BIC
Density or uncertainty surface for bivariate mixtures.
Uncertainty Plot for Model-Based Clustering
Data Simulated from a 14-Component Mixture