Simulated Example Datasets From Baudry et al. (2010)
GvHD Dataset
Model-Based Clustering
Resampling-based Inference for Gaussian finite mixture models
MclustDA discriminant analysis
Dimension reduction for model-based clustering and classification
Subset selection for GMMDR directions based on BIC.
Acidity data
Adjusted Rand Index
Swiss banknotes data
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
Simulated minefield data
Pairwise Scatter Plots showing Classification
Classification error
Internal clustCombi functions
Combining Gaussian Mixture Components for Clustering
Optimal number of clusters obtained by combining mixture components
Combining Matrix
Plot Classifications Corresponding to Successive Combined Solutions
Tree structure obtained from combining mixture components
Coordinate projections of multidimensional data modeled by an MVN mixture.
Weighted means, covariance and scattering matrices conditioning on a weighted matrix.
Simulated Cross Data
MclustDA cross-validation
Convert mixture component covariances to matrix form.
Default conjugate prior for Gaussian mixtures.
Density for Parameterized MVN Mixtures
Density Estimation via Model-Based Clustering
Diagnostic plots for 'mclustDensity' estimation
Diabetes data
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.
Plot Entropy Plots
Draw error bars on a plot
E-step for parameterized Gaussian mixture models.
E-step in the EM algorithm for a parameterized Gaussian mixture model.
Identifying Connected Components in Gaussian Finite Mixture Models for Clustering
Model-based Hierarchical Clustering
Model-based Hierarchical Clustering
Classifications from Hierarchical Agglomeration
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
Log-Likelihood of a 'Mclust' object
Log-Likelihood of a 'MclustDA' object
Majority vote
Classification given Probabilities
Correspondence between classifications.
Deprecated Functions in mclust package
Internal MCLUST functions
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.
BIC for Model-Based Clustering
Bootstrap Likelihood Ratio Test for the Number of Mixture Components
ICL Criterion for Model-Based Clustering
Best model based on BIC
MCLUST Model Names
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.
Univariate or Multivariate Normal Fit
Univariate or Multivariate Normal Fit
Number of Estimated Parameters in Gaussian Mixture Models
Number of Variance Parameters in Gaussian Mixture Models
Numeric Encoding of a Partitioning
Classifies Data According to Unique Observations
Plot Model-Based Clustering Results
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
Plot Combined Clusterings Results
Plots for Mixture-Based Density Estimate
BIC Plot for Model-Based Clustering
ICL Plot for Model-Based Clustering
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
Conjugate Prior for Gaussian Mixtures.
Random projections of multidimensional data modeled by an MVN mixture.
Random hierarchical structure
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.
Thyroid gland data
Uncertainty Plot for Model-Based Clustering
Indicator Variables given Classification
Data Simulated from a 14-Component Mixture