Baudry_etal_2010_JCGS_examples
Simulated Example Datasets From Baudry et al. (2010)
MclustSSC semi-supervised classification
Dimension reduction for model-based clustering and classification
Model-Based Clustering
GvHD Dataset
Subset selection for GMMDR directions based on BIC
Unemployment data for European countries in 2014
MclustDA discriminant analysis
Brier score to assess the accuracy of probabilistic predictions
Resampling-based Inference for Gaussian finite mixture models
Component Density for Parameterized MVN Mixture Models
Swiss banknotes data
Acidity data
Component Density for a Parameterized MVN Mixture Model
Combining Matrix
Plot Classifications Corresponding to Successive Combined Solutions
Weighted means, covariance and scattering matrices conditioning on a weighted matrix
Simulated minefield data
Simulated Cross Data
Cumulative Distribution and Quantiles for a univariate Gaussian mixture
distribution
BIC for Parameterized Gaussian Mixture Models
Adjusted Rand Index
Tree structure obtained from combining mixture components
Estimation of class prior probabilities by EM algorithm
Internal clustCombi functions
EM algorithm starting with E-step for parameterized Gaussian mixture models
Set control values for use with the EM algorithm
Classifications from Hierarchical Agglomeration
Diabetes data
Draw error bars on a plot
Density of multivariate Gaussian distribution
Majority vote
Random hierarchical structure
Log-Likelihood of a MclustDA object
Pairwise Scatter Plots showing Classification
EM algorithm starting with E-step for a parameterized Gaussian mixture model
Coordinate projections of multidimensional data modeled by an MVN mixture.
ICL for an estimated Gaussian Mixture Model
Optimal number of clusters obtained by combining mixture components
Classification error
Density Estimation via Model-Based Clustering
Model-based Hierarchical Clustering
Model-based Agglomerative Hierarchical Clustering
Combining Gaussian Mixture Components for Clustering
E-step for parameterized Gaussian mixture models.
Diagnostic plots for mclustDensity estimation
Missing data imputation via the mix package
Plot Entropy Plots
Template for variance specification for parameterized Gaussian mixture models
MCLUST Model Names
ICL Criterion for Model-Based Clustering
Plotting method for dimension reduction for model-based clustering and classification
Bootstrap Likelihood Ratio Test for the Number of Mixture Components
Classification given Probabilities
Convert mixture component covariances to matrix form
MclustDA cross-validation
Univariate or Multivariate Normal Fit
M-step for a parameterized Gaussian mixture model
Default conjugate prior for Gaussian mixtures
Numeric Encoding of a Partitioning
Log-likelihood from a table of BIC values for parameterized Gaussian mixture models
Number of Variance Parameters in Gaussian Mixture Models
Deprecated Functions in mclust package
Best model based on BIC
Plotting method for MclustSSC semi-supervised classification
Internal MCLUST functions
Density for Parameterized MVN Mixtures
Simulate from Parameterized MVN Mixture Models
Wisconsin diagnostic breast cancer (WDBC) data
Simulate from a Parameterized MVN Mixture Model
Data Simulated from a 14-Component Mixture
BIC for Model-Based Clustering
Pairwise Scatter Plots showing Missing Data Imputations
E-step in the EM algorithm for a parameterized Gaussian mixture model.
Update BIC values for parameterized Gaussian mixture models
Log-Likelihood of a Mclust object
Identifying Connected Components in Gaussian Finite Mixture Models for Clustering
Highest Density Region (HDR) Levels
Aproximate Hypervolume for Multivariate Data
BIC Plot for Model-Based Clustering
Dendrograms for Model-based Agglomerative Hierarchical Clustering
Random orthogonal matrix
Classification of multivariate observations by semi-supervised Gaussian finite mixtures
Density estimate of multivariate observations by Gaussian finite mixture modeling
Correspondence between classifications
Plot one-dimensional data modeled by an MVN mixture.
Univariate or Multivariate Normal Fit
Gaussian Mixture Modelling for Model-Based Clustering, Classification, and Density Estimation
Conjugate Prior for Gaussian Mixtures.
Random projections of multidimensional data modeled by an MVN mixture
Default values for use with MCLUST package
EM algorithm with weights starting with M-step for parameterized MVN mixture models
Convert mixture component covariances to decomposition form.
EM algorithm starting with M-step for parameterized MVN mixture models
EM algorithm starting with M-step for a parameterized Gaussian mixture model
Plot two-dimensional data modelled by an MVN mixture
M-step for parameterized Gaussian mixture models
Number of Estimated Parameters in Gaussian Mixture Models
Plot of bootstrap distributions for mixture model parameters
Plotting method for Mclust model-based clustering
Classifies Data According to Unique Observations
Plotting method for MclustDA discriminant analysis
Cluster multivariate observations by Gaussian finite mixture modeling
ICL Plot for Model-Based Clustering
Plot Combined Clusterings Results
Summarizing dimension reduction method for model-based clustering and classification
Uncertainty Plot for Model-Based Clustering
Indicator Variables given Classification
Density or uncertainty surface for bivariate mixtures
Summarizing discriminant analysis based on Gaussian finite mixture modeling
Plots for Mixture-Based Density Estimate
Classify multivariate observations on a dimension reduced subspace by Gaussian finite mixture modeling
Thyroid gland data
Classify multivariate observations by Gaussian finite mixture modeling
Summarizing semi-supervised classification model based on Gaussian finite mixtures
Summarizing Gaussian Finite Mixture Model Fits
Summary Function for Bootstrap Inference for Gaussian Finite Mixture Models
Summary function for model-based clustering via BIC