Package: RPMM 1.25

RPMM: 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.

Authors:E. Andres Houseman, Sc.D. and Devin C. Koestler, Ph.D.

RPMM_1.25.tar.gz
RPMM_1.25.zip(r-4.5)RPMM_1.25.zip(r-4.4)RPMM_1.25.zip(r-4.3)
RPMM_1.25.tgz(r-4.4-any)RPMM_1.25.tgz(r-4.3-any)
RPMM_1.25.tar.gz(r-4.5-noble)RPMM_1.25.tar.gz(r-4.4-noble)
RPMM_1.25.tgz(r-4.4-emscripten)RPMM_1.25.tgz(r-4.3-emscripten)
RPMM.pdf |RPMM.html✨
RPMM/json (API)

# Install 'RPMM' in R:
install.packages('RPMM', repos = c('https://eahouseman.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:
  • IllumBeta - DNA Methylation Data for Normal Tissue Types
  • tissue - DNA Methylation Data for Normal Tissue Types

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

50 exports 3.66 score 1 dependencies 7 dependents 20 mentions 73 scripts 1.9k downloads

Last updated 8 years agofrom:7cae8050ed. Checks:OK: 3 NOTE: 4. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 13 2024
R-4.5-winNOTESep 13 2024
R-4.5-linuxNOTESep 13 2024
R-4.4-winNOTESep 13 2024
R-4.4-macNOTESep 13 2024
R-4.3-winOKSep 13 2024
R-4.3-macOKSep 13 2024

Exports:betaEstbetaEstMultiplebetaObjfblcblcInitializeSplitDichotomizeUsingMeanblcInitializeSplitEigenblcInitializeSplitFannyblcInitializeSplitHClustblcSplitblcSplitCriterionBICblcSplitCriterionBICICLblcSplitCriterionJustRecordEverythingblcSplitCriterionLevelWtdBICblcSplitCriterionLRTblcSubTreeblcTreeblcTreeApplyblcTreeLeafClassesblcTreeLeafMatrixblcTreeOverallBICebayesgaussEstMultipleglcglcInitializeSplitEigenglcInitializeSplitFannyglcInitializeSplitHClustglcSplitglcSplitCriterionBICglcSplitCriterionBICICLglcSplitCriterionJustRecordEverythingglcSplitCriterionLevelWtdBICglcSplitCriterionLRTglcSubTreeglcTreeglcTreeApplyglcTreeLeafClassesglcTreeLeafMatrixglcTreeOverallBICglmLCllikeRPMMObjectplot.blcTreeplot.glcTreeplotImage.blcTreeplotImage.glcTreeplotTree.blcTreeplotTree.glcTreepredict.blcTreepredict.glcTreeprint.blcTreeprint.glcTree

Dependencies:cluster

Readme and manuals

Help Manual

Help pageTopics
Beta Distribution Maximum Likelihood EstimatorbetaEst
Beta Maximum Likelihood on a MatrixbetaEstMultiple
Beta Maximum Likelihood Objective FunctionbetaObjf
Beta Latent Class Modelblc
Initialize Gaussian Latent Class via Mean DichotomizationblcInitializeSplitDichotomizeUsingMean
Initialize Gaussian Latent Class via EigendecompositionblcInitializeSplitEigen
Initialize Beta Latent Class via FannyblcInitializeSplitFanny
Initialize Beta Latent Class via Hierarchical ClusteringblcInitializeSplitHClust
Beta Latent Class SplitterblcSplit
Beta RPMM Split Criterion: Use BICblcSplitCriterionBIC
Beta RPMM Split Criterion: Use ICL-BICblcSplitCriterionBICICL
Beta RPMM Split Criterion: Always Split and Record EverythingblcSplitCriterionJustRecordEverything
Beta RPMM Split Criterion: Level-Weighted BICblcSplitCriterionLevelWtdBIC
Beta RPMM Split Criterion: use likelihood ratio test p valueblcSplitCriterionLRT
Beta SubtreeblcSubTree
Beta RPMM TreeblcTree
Recursive Apply Function for Beta RPMM ObjectsblcTreeApply
Posterior Class Assignments for Beta RPMMblcTreeLeafClasses
Posterior Weight Matrix for Beta RPMMblcTreeLeafMatrix
Overall BIC for Entire RPMM Tree (Beta version)blcTreeOverallBIC
Empirical Bayes predictions for a specific RPMM modelebayes
Gaussian Maximum Likelihood on a MatrixgaussEstMultiple
Gaussian Finite Mixture Modelglc
Initialize Gaussian Latent Class via EigendecompositionglcInitializeSplitEigen
Initialize Gaussian Latent Class via FannyglcInitializeSplitFanny
Initialize Gaussian Latent Class via Hierarchical ClusteringglcInitializeSplitHClust
Gaussian Latent Class SplitterglcSplit
Gaussian RPMM Split Criterion: Use BICglcSplitCriterionBIC
Gaussian RPMM Split Criterion: Use ICL-BICglcSplitCriterionBICICL
Gaussian RPMM Split Criterion: Always Split and Record EverythingglcSplitCriterionJustRecordEverything
Gaussian RPMM Split Criterion: Level-Weighted BICglcSplitCriterionLevelWtdBIC
Gaussian RPMM Split Criterion: Use likelihood ratio test p valueglcSplitCriterionLRT
Gaussian SubtreeglcSubTree
Gaussian RPMM TreeglcTree
Recursive Apply Function for Gaussian RPMM ObjectsglcTreeApply
Posterior Class Assignments for Gaussian RPMMglcTreeLeafClasses
Posterior Weight Matrix for Gaussian RPMMglcTreeLeafMatrix
Overall BIC for Entire RPMM Tree (Gaussian version)glcTreeOverallBIC
Weighted GLM for latent class covariatesglmLC
DNA Methylation Data for Normal Tissue TypesIllumBeta IlluminaMethylation tissue
Data log-likelihood implied by a specific RPMM modelllikeRPMMObject
Plot a Beta RPMM Tree Profileplot.blcTree
Plot a Gaussian RPMM Tree Profileplot.glcTree
Plot a Beta RPMM Tree ProfileplotImage.blcTree
Plot a Gaussian RPMM Tree ProfileplotImage.glcTree
Plot a Beta RPMM Tree DendrogramplotTree.blcTree
Plot a Gaussian RPMM Tree DendrogramplotTree.glcTree
Predict using a Beta RPMM objectpredict.blcTree
Predict using a Gaussian RPMM objectpredict.glcTree
Print a Beta RPMM objectprint.blcTree
Print a Gaussian RPMM objectprint.glcTree