MixAll1.5.16 package

Clustering and Classification using Model-Based Mixture Models

plot-ClusterGamma-method

Plotting of a class [ClusterGamma]

plot-ClusterMixedDataModel-method

Plotting of a class [ClusterMixedDataModel]

plot-ClusterPoisson-method

Plotting of a class [ClusterPoisson]

plot-KmmComponent-method

Plotting of a class [KmmComponent]

ClusterAlgo-class

[ClusterAlgo] class for Cluster algorithms.

clusterAlgo

Create an instance of the [ClusterAlgo] class

ClusterAlgoPredict-class

[ClusterAlgoPredict] class for predict algorithm.

clusterAlgoPredict

Create an instance of the [ClusterAlgoPredict] class

ClusterCategorical-class

Definition of the [ClusterCategorical] class

clusterCategorical

Create an instance of the [ClusterCategorical] class

ClusterCategoricalComponent-class

Definition of the [ClusterCategoricalComponent] class

clusterCategoricalNames

Create a vector of Categorical mixture model names.

ClusterDiagGaussian-class

Definition of the [ClusterDiagGaussian] class

clusterDiagGaussian

Create an instance of the [ClusterDiagGaussian] class

ClusterDiagGaussianComponent-class

Definition of the [ClusterDiagGaussianComponent] class

clusterDiagGaussianNames

Create a vector of diagonal Gaussian mixture model names.

ClusterGamma-class

Definition of the [ClusterGamma] class

clusterGamma

Create an instance of the [ClusterGamma] class

ClusterGammaComponent-class

Definition of the [ClusterGammaComponent] class

clusterGammaNames

Create a vector of gamma mixture model names.

ClusterInit-class

Constructor of the [ClusterInit] class

KmmComponent-class

Definition of the [KmmComponent] class

clusterInit

Create an instance of [ClusterInit] class

clusterMixedData

Create an instance of the [ClusterMixedDataModel] class

ClusterMixedDataModel-class

Definition of the [ClusterMixedDataModel] class

ClusterModels-class

Interface base Class [IClusterModel] for Cluster models.

ClusterPoisson-class

Definition of the [ClusterPoisson] class

MixAll-package

MixAll Allows to estimate parametric mixture models with mixed data se...

clusterPoisson

Create an instance of the [ClusterPoisson] class

clusterPoissonNames

Create a vector of Poisson mixture model names.

ClusterPredict-class

Class [ClusterPredict] for predicting

clusterPredict

Create an instance of [ClusterPredict] class

ClusterPredictMixedData-class

Class [ClusterPredictMixedData] for predicting

ClusterStrategy-class

Constructor of [ClusterStrategy] class

clusterStrategy

A strategy is a multistage empirical process for finding a good estima...

extract-methods

Extract parts of a MixAll S4 class

HeartDisease

Mixed data : Cleveland Heart Disease Data

IClusterComponent-class

Definition of the [IClusterComponent] class

IClusterPredict-class

Interface class [IClusterPredict] for predicting

initialize-methods

Initialize an instance of a MixAll S4 class.

kmm

Create an instance of the [KmmModel] class

plot-ClusterCategorical-method

Plotting of a class [ClusterCategorical]

kmmMixedData

Create an instance of the [KmmMixedDataModel] class

KmmMixedDataModel-class

Definition of the [KmmMixedDataModel] class

KmmModel-class

Definition of the [KmmModel] class

kmmNames

Create a vector of Kernel mixture model (KMM) names.

kmmStrategy

Create an instance of [ClusterStrategy] class

LearnAlgo-class

[LearnAlgo] class for Cluster algorithms.

plot-ClusterDiagGaussian-method

Plotting of a class [ClusterDiagGaussian]

learnAlgo

Create an instance of the [LearnAlgo] class

learners

Create an instance of a learn mixture model

learnMixedData

This function learn the optimal mixture model when the class labels ar...

missingValues-methods

Return the missing values of a component or a cluster class.

plot-KmmMixedDataModel-method

Plotting of a class [KmmMixedDataModel]

plot-KmmModel-method

Plotting of a class [KmmModel]

print-methods

Print a MixAll S4 class to standard output.

show-methods

Show description of a MixAll S4 class to standard output.

summary-methods

Produce summary of a MixAll S4 class.

Algorithms and methods for model-based clustering and classification. It supports various types of data: continuous, categorical and counting and can handle mixed data of these types. It can fit Gaussian (with diagonal covariance structure), gamma, categorical and Poisson models. The algorithms also support missing values.

  • Maintainer: Serge Iovleff
  • License: GPL (>= 2)
  • Last published: 2024-05-15