RPMM v1.20

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Recursively Partitioned Mixture Model

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.

Functions in RPMM

Name Description
betaEst Beta Distribution Maximum Likelihood Estimator
blcTree Beta RPMM Tree
IlluminaMethylation DNA Methylation Data for Normal Tissue Types
blcTreeLeafClasses Posterior Class Assignments for Beta RPMM
blcTreeLeafMatrix Posterior Weight Matrix for Beta RPMM
blcSplitCriterionBICICL Beta RPMM Split Criterion: Use ICL-BIC
blcInitializeSplitHClust Initialize Beta Latent Class via Hierarchical Clustering
blcTreeApply Recursive Apply Function for Beta RPMM Objects
glcSplitCriterionBICICL Gaussian RPMM Split Criterion: Use ICL-BIC
glcInitializeSplitEigen Initialize Gaussian Latent Class via Eigendecomposition
plot.blcTree Plot a Beta RPMM Tree Profile
glmLC Weighted GLM for latent class covariates
blcTreeOverallBIC Overall BIC for Entire RPMM Tree (Beta version)
glcInitializeSplitHClust Initialize Gaussian Latent Class via Hierarchical Clustering
llikeRPMMObject Data log-likelihood implied by a specific RPMM model
glcSubTree Gaussian Subtree
blcSplit Beta Latent Class Splitter
blcSplitCriterionJustRecordEverything Beta RPMM Split Criterion: Always Split and Record Everything
print.blcTree Print a Beta RPMM object
ebayes Empirical Bayes predictions for a specific RPMM model
blcSplitCriterionLevelWtdBIC Beta RPMM Split Criterion: Level-Weighted BIC
plotTree.glcTree Plot a Gaussian RPMM Tree Dendrogram
glcTreeOverallBIC Overall BIC for Entire RPMM Tree (Gaussian version)
glcSplitCriterionJustRecordEverything Gaussian RPMM Split Criterion: Always Split and Record Everything
gaussEstMultiple Gaussian Maximum Likelihood on a Matrix
betaEstMultiple Beta Maximum Likelihood on a Matrix
blcInitializeSplitDichotomizeUsingMean Initialize Gaussian Latent Class via Mean Dichotomization
glcTreeLeafMatrix Posterior Weight Matrix for Gaussian RPMM
predict.glcTree Predict using a Gaussian RPMM object
glcSplitCriterionLevelWtdBIC Gaussian RPMM Split Criterion: Level-Weighted BIC
betaObjf Beta Maximum Likelihood Objective Function
blc Beta Latent Class Model
plotImage.glcTree Plot a Gaussian RPMM Tree Profile
glcSplitCriterionBIC Gaussian RPMM Split Criterion: Use BIC
glcSplitCriterionLRT Gaussian RPMM Split Criterion: Use likelihood ratio test p value
blcSplitCriterionLRT Beta RPMM Split Criterion: use likelihood ratio test p value
blcInitializeSplitFanny Initialize Beta Latent Class via Fanny
glc Gaussian Finite Mixture Model
plotImage.blcTree Plot a Beta RPMM Tree Profile
plotTree.blcTree Plot a Beta RPMM Tree Dendrogram
glcSplit Gaussian Latent Class Splitter
blcInitializeSplitEigen Initialize Gaussian Latent Class via Eigendecomposition
print.glcTree Print a Gaussian RPMM object
glcInitializeSplitFanny Initialize Gaussian Latent Class via Fanny
blcSplitCriterionBIC Beta RPMM Split Criterion: Use BIC
glcTreeApply Recursive Apply Function for Gaussian RPMM Objects
blcSubTree Beta Subtree
glcTreeLeafClasses Posterior Class Assignments for Gaussian RPMM
plot.glcTree Plot a Gaussian RPMM Tree Profile
predict.blcTree Predict using a Beta RPMM object
glcTree Gaussian RPMM Tree
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Details

Type Package
Date 2014-09-13
License GPL (>= 2)
Packaged 2014-09-13 16:23:47 UTC; housemae
NeedsCompilation no
Repository CRAN
Date/Publication 2014-09-14 07:22:44

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