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MDIr

Overfitted Bayesian mixture models and Multiple Dataset Integration. Kernels allowed are

  • G: Gaussian (independent measurements/diagonal covariance matrix).
  • MVN: Multivariate normal (full covariance matrix).
  • C: Categorical.
  • GP: Gaussian process (squared exponential covariance kernel).
  • TAGM: t-augmented Gaussian (MVN with a global MVT component for outliers).
  • TAGPM: t-augmented Gaussian Process (GP with a global MVT component for outliers).

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Install

install.packages('mdir')

Monthly Downloads

8

Version

0.9.0

License

GPL-3

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Maintainer

Stephen Coleman

Last Published

May 17th, 2023

Functions in mdir (0.9.0)

comparePSMsAcrossChains

Compare Posterior Similarity Matrices Across Chains
compileConsensusClustering

Compile Consensus Clustering
generateInitialLabels

Generate initial labels
gammaLogLikelihood

Gamma log-likelihood
generateSimulationDataset

Generate simulation dataset
generateInitialUnsupervisedLabels

Generate initial unsupervised labels
generateGaussianDataset

Generate Gaussian dataset
mvtLogLikelihood

Multivariate t log-likelihood
getLikelihood

Get likelihood
pNorm

Multivariate Normal log-likelihood
invGammaLogLikelihood

Inverse gamma log-likelihood
plotLikelihoods

Plot likelihoods
prepDataForggHeatmap

Prepare data for ggplot heatmap
prepCMssForGGplot

Prepare consensus matrices for ggplot
setupOutlierComponents

Setup outlier components
findOrder

Find order
makeCMComparisonSummaryDF

Make CM Comparison Summary DF
stickBreakingPrior

Stick breaking prior
processProposalWindows

Process proposal windows
invWishartLogLikelihood

Inverse-Wishart log-likelihood
processMCMCChains

Process MCMC chains
createSimilarityMat

Create Similarity Matrix
wishartLogLikelihood

Wishart log-likelihood
generateInitialSemiSupervisedLabels

Generate initial semi-sueprvised labels
predictFromMultipleChains

Predict from multiple MCMC chains
sampleStickBreakingPrior

Sample stick breaking prior
prepSimilarityMatricesForGGplot

Prepare similarity matrices for ggplot
processMCMCChain

Process MCMC chain
sampleViewPriorLabels

Sample view prior labels
runMCMCChains

Run MCMC Chains
runMDI

Call Multiple Dataset Integration
tagmReDraft-package

Semi-supervised Multiple Dataset Integration
translateTypes

Translate types
checkLabels

Check labels
checkFixedInput

Check fixed input
calcAllocProb

Calculate allocation probabilities
checkTypes

Check types
callMDI

Call Multiple Dataset Integration
checkNumberOfSamples

Check number of samples
calcFusionProbabiliy

Calculate fusion probability
checkDataCorrectInput

Check data correct input
callMixtureModel

Call mixture model
calcFusionProbabiliyAllViews

Calculate fusion probability all views