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