BIC for Parameterized Gaussian Mixture Models
Component Density for a Parameterized MVN Mixture Model
Cumulative Distribution and Quantiles for a univariate Gaussian mixture
distribution
Default conjugate prior for Gaussian mixtures.
Aproximate Hypervolume for Multivariate Data
Density for Parameterized MVN Mixtures
Gaussian Mixture Modelling for Model-Based Clustering, Classification, and Density Estimation
Highest Density Region (HDR) Levels
Density Estimation via Model-Based Clustering
Diagnostic plots for mclustDensity
estimation
Component Density for Parameterized MVN Mixture Models
Default values for use with MCLUST package
Bootstrap Likelihood Ratio Test for the Number of Mixture Components
ICL Criterion for Model-Based Clustering
Simulated minefield data
Dimension reduction for model-based clustering and classification
Classification error
Classifies Data According to Unique Observations
Weighted means, covariance and scattering matrices conditioning on a weighted matrix.
Pairwise Scatter Plots showing Classification
Plot Model-Based Clustering Results
Internal clustCombi functions
Simulated Cross Data
Combining Gaussian Mixture Components for Clustering
Tree structure obtained from combining mixture components
Set control values for use with the EM algorithm.
Optimal number of clusters obtained by combining mixture components
Combining Matrix
EM algorithm starting with E-step for a parameterized Gaussian mixture model.
Plots for Mixture-Based Density Estimate
Coordinate projections of multidimensional data modeled by an MVN mixture.
Plot Classifications Corresponding to Successive Combined Solutions
BIC Plot for Model-Based Clustering
Convert mixture component covariances to decomposition form.
MclustDA cross-validation
Simulate from Parameterized MVN Mixture Models
Convert mixture component covariances to matrix form.
E-step for parameterized Gaussian mixture models.
Plot Entropy Plots
Diabetes data
Summarizing dimension reduction method for model-based clustering and classification
Pairwise Scatter Plots showing Missing Data Imputations
Log-Likelihood of a Mclust
object
Deprecated Functions in mclust package
Draw error bars on a plot
Summary function for model-based clustering via BIC
E-step in the EM algorithm for a parameterized Gaussian mixture model.
Model-based Hierarchical Clustering
Internal MCLUST functions
Classifications from Hierarchical Agglomeration
MCLUST Model Names
Majority vote
Log-Likelihood of a MclustDA
object
EM algorithm starting with E-step for parameterized Gaussian mixture models.
ICL for an estimated Gaussian Mixture Model
EM algorithm starting with M-step for parameterized MVN mixture models.
Template for variance specification for parameterized Gaussian mixture models
Missing data imputation via the mix package
Identifying Connected Components in Gaussian Finite Mixture Models for Clustering
M-step for a parameterized Gaussian mixture model.
EM algorithm with weights starting with M-step for parameterized MVN mixture models
Plot two-dimensional data modelled by an MVN mixture.
Plot one-dimensional data modeled by an MVN mixture.
Univariate or Multivariate Normal Fit
Univariate or Multivariate Normal Fit
Number of Estimated Parameters in Gaussian Mixture Models
BIC for Model-Based Clustering
Plotting method for dimension reduction for model-based clustering and classification
Plot of bootstrap distributions for mixture model parameters
Update BIC values for parameterized Gaussian mixture models
Plotting method for MclustDA discriminant analysis
EM algorithm starting with M-step for a parameterized Gaussian mixture model.
ICL Plot for Model-Based Clustering
M-step for parameterized Gaussian mixture models.
Plot Combined Clusterings Results
Density estimate of multivariate observations by Gaussian finite mixture modeling
Random projections of multidimensional data modeled by an MVN mixture.
Model-based Hierarchical Clustering
Classification given Probabilities
Cluster multivariate observations by Gaussian finite mixture modeling
Summary Function for Bootstrap Inference for Gaussian Finite Mixture Models
Uncertainty Plot for Model-Based Clustering
Density or uncertainty surface for bivariate mixtures
Summarizing discriminant analysis based on Gaussian finite mixture modeling.
Conjugate Prior for Gaussian Mixtures.
Thyroid gland data
Random hierarchical structure
Indicator Variables given Classification
Data Simulated from a 14-Component Mixture
Correspondence between classifications.
Log-likelihood from a table of BIC values for parameterized Gaussian mixture models
Best model based on BIC
Number of Variance Parameters in Gaussian Mixture Models
Classify multivariate observations by Gaussian finite mixture modeling
Numeric Encoding of a Partitioning
Classify multivariate observations on a dimension reduced subspace by Gaussian finite mixture modeling
Simulate from a Parameterized MVN Mixture Model
Summarizing Gaussian Finite Mixture Model Fits
MclustDA discriminant analysis
Model-Based Clustering
Adjusted Rand Index
Resampling-based Inference for Gaussian finite mixture models
Subset selection for GMMDR directions based on BIC.
Swiss banknotes data
Acidity data
Baudry_etal_2010_JCGS_examples
Simulated Example Datasets From Baudry et al. (2010)
GvHD Dataset