Package index • NPflow

All functions

DPMGibbsN(): Slice Sampling of the Dirichlet Process Mixture Model with a prior on alpha

DPMGibbsN_SeqPrior(): Slice Sampling of Dirichlet Process Mixture of Gaussian distributions

DPMGibbsN_parallel(): Slice Sampling of the Dirichlet Process Mixture Model with a prior on alpha

DPMGibbsSkewN(): Slice Sampling of Dirichlet Process Mixture of skew normal distributions

DPMGibbsSkewN_parallel(): Parallel Implementation of Slice Sampling of Dirichlet Process Mixture of skew normal distributions

DPMGibbsSkewT(): Slice Sampling of Dirichlet Process Mixture of skew Student's t-distributions

DPMGibbsSkewT_SeqPrior(): Slice Sampling of Dirichlet Process Mixture of skew Student's t-distributions

DPMGibbsSkewT_SeqPrior_parallel(): Slice Sampling of Dirichlet Process Mixture of skew Student's t-distributions

DPMGibbsSkewT_parallel(): Slice Sampling of Dirichlet Process Mixture of skew Student's t-distributions

DPMpost(): Posterior estimation for Dirichlet process mixture of multivariate (potentially skew) distributions models

Flimited(): Compute a limited F-measure

FmeasureC(): C++ implementation of the F-measure computation

FmeasureC_no0(): C++ implementation of the F-measure computation without the reference class 0

Fmeasure_costC(): Multiple cost computations with the F-measure as the loss function

MAP_sNiW_mmEM() MAP_sNiW_mmEM_weighted() MAP_sNiW_mmEM_vague(): EM MAP for mixture of sNiW

MLE_NiW_mmEM(): EM MLE for mixture of NiW

MLE_gamma(): MLE for Gamma distribution

MLE_sNiW(): MLE for sNiW distributed observations

MLE_sNiW_mmEM(): EM MLE for mixture of sNiW

NPflow-package NPflow: Bayesian Nonparametrics for Automatic Gating of Flow Cytometry data

NuMatParC(): C++ implementation of similarity matrix computation using pre-computed distances

burn.DPMMclust(): Burning MCMC iterations from a Dirichlet Process Mixture Model.

cluster_est_Fmeasure(): Point estimate of the partition using the F-measure as the cost function.

cluster_est_Mbinder_norm(): Point estimate of the partition using a modified Binder loss function

cluster_est_binder(): Point estimate of the partition for the Binder loss function

cluster_est_pear(): Gets a point estimate of the partition using posterior expected adjusted Rand index (PEAR)

cytoScatter(): Scatterplot of flow cytometry data

evalClustLoss(): ELoss of a partition point estimate compared to a gold standard

lgamma_mv(): Multivariate log gamma function

print(<summaryDPMMclust>) plot(<summaryDPMMclust>): Methods for a summary of a DPMMclust object

mmNiWpdf(): multivariate Normal inverse Wishart probability density function for multiple inputs

mmNiWpdfC(): C++ implementation of multivariate Normal inverse Wishart probability density function for multiple inputs

mmsNiWlogpdf(): Probability density function of multiple structured Normal inverse Wishart

mmsNiWpdfC(): C++ implementation of multivariate structured Normal inverse Wishart probability density function for multiple inputs

mmvnpdfC(): C++ implementation of multivariate Normal probability density function for multiple inputs

mmvsnpdfC(): C++ implementation of multivariate skew Normal probability density function for multiple inputs

mmvstpdfC(): C++ implementation of multivariate Normal probability density function for multiple inputs

mmvtpdfC(): C++ implementation of multivariate Normal probability density function for multiple inputs

mvnlikC(): C++ implementation of multivariate Normal probability density function for multiple inputs

mvnpdf(): multivariate-Normal probability density function

mvnpdfC(): C++ implementation of multivariate normal probability density function for multiple inputs

mvsnlikC(): C++ implementation of multivariate skew normal likelihood function for multiple inputs

mvsnpdf(): multivariate Skew-Normal probability density function

mvstlikC(): C++ implementation of multivariate skew t likelihood function for multiple inputs

mvstpdf(): multivariate skew-t probability density function

mvtpdf(): multivariate Student's t-distribution probability density function

plot_ConvDPM(): Convergence diagnostic plots

plot_DPM(): Plot of a Dirichlet process mixture of gaussian distribution partition

plot_DPMsn(): Plot of a Dirichlet process mixture of skew normal distribution partition

plot_DPMst(): Plot of a Dirichlet process mixture of skew t-distribution partition

postProcess.DPMMclust(): Post-processing Dirichlet Process Mixture Models results to get a mixture distribution of the posterior locations

priormix(): Construction of an Empirical based prior

rCRP(): Generating cluster data from the Chinese Restaurant Process

sample_alpha(): Sampler for the concentration parameter of a Dirichlet process

similarityMat(): Computes the co-clustering (or similarity) matrix

similarityMatC(): C++ implementation

similarityMat_nocostC(): C++ implementation

summary(<DPMMclust>): Summarizing Dirichlet Process Mixture Models

vclust2mcoclustC(): C++ implementation