Simulated Example Datasets From Baudry et al. (2010)
Brier score to assess the accuracy of probabilistic predictions
Unemployment data for European countries in 2014
Resampling-based Inference for Gaussian finite mixture models
Dimension reduction for model-based clustering and classification
Subset selection for GMMDR directions based on BIC
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
Estimation of class prior probabilities by EM algorithm
Combining Gaussian Mixture Components for Clustering
Optimal number of clusters obtained by combining mixture components
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
Convert mixture component covariances to matrix form
Default conjugate prior for Gaussian mixtures
Density Estimation via Model-Based Clustering
Diagnostic plots for 'mclustDensity' estimation
Density of multivariate Gaussian distribution
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.
Identifying Connected Components in Gaussian Finite Mixture Models for Clustering
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
Gaussian Mixture Modelling 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
Update BIC values for parameterized Gaussian mixture models
Bootstrap Likelihood Ratio Test for the Number of Mixture Components
ICL Criterion for Model-Based Clustering
Log-likelihood from a table of BIC values for parameterized Gaussian mixture models
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
Plotting method for Mclust model-based clustering
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
Plotting method for MclustSSC semi-supervised classification
Plots for Mixture-Based Density Estimate
Dendrograms for Model-based Agglomerative Hierarchical 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
Classification of multivariate observations by semi-supervised Gaussian finite mixtures
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
Summarizing semi-supervised classification model based on Gaussian finite mixtures
Summary function for model-based clustering via BIC
Density or uncertainty surface for bivariate mixtures
Uncertainty Plot for Model-Based Clustering
Wisconsin diagnostic breast cancer (WDBC) data
Data Simulated from a 14-Component Mixture