RPMM1.25 package

Recursively Partitioned Mixture Model

betaEst

Beta Distribution Maximum Likelihood Estimator

betaEstMultiple

Beta Maximum Likelihood on a Matrix

betaObjf

Beta Maximum Likelihood Objective Function

blc

Beta Latent Class Model

blcInitializeSplitDichotomizeUsingMean

Initialize Gaussian Latent Class via Mean Dichotomization

blcInitializeSplitEigen

Initialize Gaussian Latent Class via Eigendecomposition

blcInitializeSplitFanny

Initialize Beta Latent Class via Fanny

blcInitializeSplitHClust

Initialize Beta Latent Class via Hierarchical Clustering

blcSplit

Beta Latent Class Splitter

blcSplitCriterionBIC

Beta RPMM Split Criterion: Use BIC

blcSplitCriterionBICICL

Beta RPMM Split Criterion: Use ICL-BIC

blcSplitCriterionJustRecordEverything

Beta RPMM Split Criterion: Always Split and Record Everything

blcSplitCriterionLevelWtdBIC

Beta RPMM Split Criterion: Level-Weighted BIC

blcSplitCriterionLRT

Beta RPMM Split Criterion: use likelihood ratio test p value

blcSubTree

Beta Subtree

blcTree

Beta RPMM Tree

blcTreeApply

Recursive Apply Function for Beta RPMM Objects

blcTreeLeafClasses

Posterior Class Assignments for Beta RPMM

blcTreeLeafMatrix

Posterior Weight Matrix for Beta RPMM

blcTreeOverallBIC

Overall BIC for Entire RPMM Tree (Beta version)

ebayes

Empirical Bayes predictions for a specific RPMM model

gaussEstMultiple

Gaussian Maximum Likelihood on a Matrix

glc

Gaussian Finite Mixture Model

glcInitializeSplitEigen

Initialize Gaussian Latent Class via Eigendecomposition

glcInitializeSplitFanny

Initialize Gaussian Latent Class via Fanny

glcInitializeSplitHClust

Initialize Gaussian Latent Class via Hierarchical Clustering

glcSplit

Gaussian Latent Class Splitter

glcSplitCriterionBIC

Gaussian RPMM Split Criterion: Use BIC

glcSplitCriterionBICICL

Gaussian RPMM Split Criterion: Use ICL-BIC

glcSplitCriterionJustRecordEverything

Gaussian RPMM Split Criterion: Always Split and Record Everything

glcSplitCriterionLevelWtdBIC

Gaussian RPMM Split Criterion: Level-Weighted BIC

glcSplitCriterionLRT

Gaussian RPMM Split Criterion: Use likelihood ratio test p value

glcSubTree

Gaussian Subtree

glcTree

Gaussian RPMM Tree

glcTreeApply

Recursive Apply Function for Gaussian RPMM Objects

glcTreeLeafClasses

Posterior Class Assignments for Gaussian RPMM

glcTreeLeafMatrix

Posterior Weight Matrix for Gaussian RPMM

glcTreeOverallBIC

Overall BIC for Entire RPMM Tree (Gaussian version)

glmLC

Weighted GLM for latent class covariates

llikeRPMMObject

Data log-likelihood implied by a specific RPMM model

plot.blcTree

Plot a Beta RPMM Tree Profile

plot.glcTree

Plot a Gaussian RPMM Tree Profile

plotImage.blcTree

Plot a Beta RPMM Tree Profile

plotImage.glcTree

Plot a Gaussian RPMM Tree Profile

plotTree.blcTree

Plot a Beta RPMM Tree Dendrogram

plotTree.glcTree

Plot a Gaussian RPMM Tree Dendrogram

predict.blcTree

Predict using a Beta RPMM object

predict.glcTree

Predict using a Gaussian RPMM object

print.blcTree

Print a Beta RPMM object

print.glcTree

Print a Gaussian RPMM object

Recursively Partitioned Mixture Model for Beta and Gaussian Mixtures. This is a model-based clustering algorithm that returns a hierarchy of classes, similar to hierarchical clustering, but also similar to finite mixture models.

  • Maintainer: E. Andres Houseman
  • License: GPL (>= 2)
  • Last published: 2017-02-28