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Help pages:
| e1071::bclust | Bagged Clustering | |
| e1071::cmeans | Fuzzy C-Means Clustering | |
| e1071::cshell | Fuzzy C-Shell Clustering | |
| e1071::fclustIndex | Fuzzy Cluster Indexes (Validity/Performance Measures) | |
| e1071::lca | Latent Class Analysis (LCA) | |
| mclust::Mclust | Model-Based Clustering | |
| mclust::MclustBootstrap | Resampling-based Inference for Gaussian finite mixture models | |
| mclust::adjustedRandIndex | Adjusted Rand Index | |
| mclust::bic | BIC for Parameterized Gaussian Mixture Models | |
| mclust::cdens | Component Density for Parameterized MVN Mixture Models | |
| mclust::cdensE | Component Density for a Parameterized MVN Mixture Model | |
| mclust::cdfMclust | Cumulative Distribution and Quantiles for a univariate Gaussian mixture distribution | |
| mclust::clPairs | Pairwise Scatter Plots showing Classification | |
| mclust::classError | Classification error | |
| mclust::clustCombi | Combining Gaussian Mixture Components for Clustering | |
| mclust::clustCombiOptim | Optimal number of clusters obtained by combining mixture components | |
| mclust::combMat | Combining Matrix | |
| mclust::combiPlot | Plot Classifications Corresponding to Successive Combined Solutions | |
| mclust::combiTree | Tree structure obtained from combining mixture components | |
| mclust::coordProj | Coordinate projections of multidimensional data modeled by an MVN mixture. | |
| mclust::decomp2sigma | Convert mixture component covariances to matrix form | |
| mclust::defaultPrior | Default conjugate prior for Gaussian mixtures | |
| mclust::dens | Density for Parameterized MVN Mixtures | |
| mclust::densityMclust | Density Estimation via Model-Based Clustering | |
| mclust::densityMclust.diagnostic | Diagnostic plots for 'mclustDensity' estimation | |
| mclust::dupPartition | Partition the data by grouping together duplicated data | |
| mclust::em | EM algorithm starting with E-step for parameterized Gaussian mixture models | |
| mclust::emControl | Set control values for use with the EM algorithm | |
| mclust::emE | EM algorithm starting with E-step for a parameterized Gaussian mixture model | |
| mclust::entPlot | Plot Entropy Plots | |
| mclust::estep | E-step for parameterized Gaussian mixture models. | |
| mclust::estepE | E-step in the EM algorithm for a parameterized Gaussian mixture model. | |
| mclust::gmmhd | Identifying Connected Components in Gaussian Finite Mixture Models for Clustering | |
| mclust::hc | Model-based Agglomerative Hierarchical Clustering | |
| mclust::hcE | Model-based Hierarchical Clustering | |
| mclust::hcRandomPairs | Random hierarchical structure | |
| mclust::hclass | Classifications from Hierarchical Agglomeration | |
| mclust::hypvol | Aproximate Hypervolume for Multivariate Data | |
| mclust::icl | ICL for an estimated Gaussian Mixture Model | |
| mclust::imputeData | Missing data imputation via the 'mix' package | |
| mclust::imputePairs | Pairwise Scatter Plots showing Missing Data Imputations | |
| mclust::map | Classification given Probabilities | |
| mclust::mapClass | Correspondence between classifications | |
| mclust::mclust.options | Default values for use with MCLUST package | |
| mclust::mclust1Dplot | Plot one-dimensional data modeled by an MVN mixture. | |
| mclust::mclust2Dplot | Plot two-dimensional data modelled by an MVN mixture | |
| mclust::mclustBIC | BIC for Model-Based Clustering | |
| mclust::mclustBICupdate | Update BIC values for parameterized Gaussian mixture models | |
| mclust::mclustBootstrapLRT | Bootstrap Likelihood Ratio Test for the Number of Mixture Components | |
| mclust::mclustICL | ICL Criterion for Model-Based Clustering | |
| mclust::mclustLoglik | Log-likelihood from a table of BIC values for parameterized Gaussian mixture models | |
| mclust::mclustModel | Best model based on BIC | |
| mclust::mclustModelNames | MCLUST Model Names | |
| mclust::mclustVariance | Template for variance specification for parameterized Gaussian mixture models | |
| mclust::me | EM algorithm starting with M-step for parameterized MVN mixture models | |
| mclust::meE | EM algorithm starting with M-step for a parameterized Gaussian mixture model | |
| mclust::mstep | M-step for parameterized Gaussian mixture models | |
| mclust::mstepE | M-step for a parameterized Gaussian mixture model | |
| mclust::mvn | Univariate or Multivariate Normal Fit | |
| mclust::mvnX | Univariate or Multivariate Normal Fit | |
| mclust::nMclustParams | Number of Estimated Parameters in Gaussian Mixture Models | |
| mclust::nVarParams | Number of Variance Parameters in Gaussian Mixture Models | |
| mclust::partconv | Numeric Encoding of a Partitioning | |
| mclust::partuniq | Classifies Data According to Unique Observations | |
| mclust::plot.Mclust | Plotting method for Mclust model-based clustering | |
| mclust::plot.MclustBootstrap | Plot of bootstrap distributions for mixture model parameters | |
| mclust::plot.clustCombi | Plot Combined Clusterings Results | |
| mclust::plot.densityMclust | Plots for Mixture-Based Density Estimate | |
| mclust::plot.hc | Dendrograms for Model-based Agglomerative Hierarchical Clustering | |
| mclust::plot.mclustBIC | BIC Plot for Model-Based Clustering | |
| mclust::plot.mclustICL | ICL Plot for Model-Based Clustering | |
| mclust::priorControl | Conjugate Prior for Gaussian Mixtures. | |
| mclust::randProj | Random projections of multidimensional data modeled by an MVN mixture | |
| mclust::sigma2decomp | Convert mixture component covariances to decomposition form. | |
| mclust::sim | Simulate from Parameterized MVN Mixture Models | |
| mclust::simE | Simulate from a Parameterized MVN Mixture Model | |
| mclust::summary.Mclust | Summarizing Gaussian Finite Mixture Model Fits | |
| mclust::summary.MclustBootstrap | Summary Function for Bootstrap Inference for Gaussian Finite Mixture Models | |
| mclust::summary.mclustBIC | Summary function for model-based clustering via BIC | |
| mclust::surfacePlot | Density or uncertainty surface for bivariate mixtures | |
| mclust::uncerPlot | Uncertainty Plot for Model-Based Clustering | |
| mclust::unmap | Indicator Variables given Classification | |
| proxy::dist | Matrix Distance/Similarity Computation | |
| proxy::pr_DB | Registry of proximities | |
| proxy::rowSums.dist | Row Sums/Means of Sparse Symmetric Matrices | |
| cluster::agnes | Agglomerative Nesting (Hierarchical Clustering) | |
| cluster::agnes.object | Agglomerative Nesting (AGNES) Object | |
| cluster::bannerplot | Plot Banner (of Hierarchical Clustering) | |
| cluster::clara | Clustering Large Applications | |
| cluster::clara.object | Clustering Large Applications (CLARA) Object | |
| cluster::clusGap | Gap Statistic for Estimating the Number of Clusters | |
| cluster::clusplot.default | Bivariate Cluster Plot (clusplot) Default Method | |
| cluster::clusplot | Bivariate Cluster Plot (of a Partitioning Object) | |
| cluster::coefHier | Agglomerative / Divisive Coefficient for 'hclust' Objects | |
| cluster::daisy | Dissimilarity Matrix Calculation | |
| cluster::diana | DIvisive ANAlysis Clustering | |
| cluster::dissimilarity.object | Dissimilarity Matrix Object | |
| cluster::fanny | Fuzzy Analysis Clustering | |
| cluster::fanny.object | Fuzzy Analysis (FANNY) Object | |
| cluster::medoids | Compute 'pam'-consistent Medoids from Clustering | |
| cluster::mona | MONothetic Analysis Clustering of Binary Variables | |
| cluster::mona.object | Monothetic Analysis (MONA) Object | |
| cluster::pam | Partitioning Around Medoids | |
| cluster::pam.object | Partitioning Around Medoids (PAM) Object | |
| cluster::partition | Partitioning Object | |
| cluster::plot.agnes | Plots of an Agglomerative Hierarchical Clustering | |
| cluster::plot.diana | Plots of a Divisive Hierarchical Clustering | |
| cluster::plot.mona | Banner of Monothetic Divisive Hierarchical Clusterings | |
| cluster::plot.partition | Plot of a Partition of the Data Set | |
| cluster::pltree | Plot Clustering Tree of a Hierarchical Clustering | |
| cluster::print.agnes | Print Method for AGNES Objects | |
| cluster::print.clara | Print Method for CLARA Objects | |
| cluster::print.diana | Print Method for DIANA Objects | |
| cluster::print.dissimilarity | Print and Summary Methods for Dissimilarity Objects | |
| cluster::print.fanny | Print and Summary Methods for FANNY Objects | |
| cluster::print.mona | Print Method for MONA Objects | |
| cluster::print.pam | Print Method for PAM Objects | |
| cluster::silhouette | Compute or Extract Silhouette Information from Clustering | |
| cluster::summary.agnes | Summary Method for 'agnes' Objects | |
| cluster::summary.clara | Summary Method for 'clara' Objects | |
| cluster::summary.diana | Summary Method for 'diana' Objects | |
| cluster::summary.mona | Summary Method for 'mona' Objects | |
| cluster::summary.pam | Summary Method for PAM Objects | |
| cluster::twins.object | Hierarchical Clustering Object | |
| stats::as.hclust | Convert Objects to Class hclust | |
| stats::cophenetic | Cophenetic Distances for a Hierarchical Clustering | |
| stats::cutree | Cut a Tree into Groups of Data | |
| stats::dist | Distance Matrix Computation | |
| stats::hclust | Hierarchical Clustering | |
| stats::identify.hclust | Identify Clusters in a Dendrogram | |
| stats::kmeans | K-Means Clustering | |
| stats::rect.hclust | Draw Rectangles Around Hierarchical Clusters |