R: Recursively Partitioned Mixture Model

# Recursively Partitioned Mixture Model

## Documentation for package ‘RPMM’ version 1.25

- DESCRIPTION file

## Help Pages

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
IllumBeta | DNA Methylation Data for Normal Tissue Types
IlluminaMethylation | DNA Methylation Data for Normal Tissue Types
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
tissue | DNA Methylation Data for Normal Tissue Types