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NPflow (version 0.10.1)

Bayesian Nonparametrics for Automatic Gating of Flow-Cytometry Data

Description

Dirichlet process mixture of multivariate normal, skew normal or skew t-distributions modeling oriented towards flow-cytometry data pre-processing applications.

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Install

install.packages('NPflow')

Monthly Downloads

276

Version

0.10.1

License

LGPL-3 | file LICENSE

Maintainer

Boris P Hejblum

Last Published

May 3rd, 2016

Functions in NPflow (0.10.1)

Posterior estimation for Dirichlet process mixture of multivariate (potentially skew) distibutions models

multivariate skew-t probability density function

EM MLE for mixture of NiW

postProcess.DPMMclust

Post-processing Dirichlet Process Mixture Models results to get a mixture distribution of the posterior locations

Convergence diagnostic plots

DPMGibbsSkewT_SeqPrior_parallel

Slice Sampling of Dirichlet Process Mixture of skew Students's t-distibutions

C++ implementation of multivariate Normal inverse Wishart probability density function for multiple inputs

C++ implementation of residual trace computation step used when sampling the scale

EM MAP for mixture of sNiW

Plot of a Dirichlet process mixture of gaussian distribution partition

cluster_est_Mbinder_norm

Point estimate of the partition using a modified Binder loss function

multivariate-Normal probability density function

multivariate Skew-Normal probability density function

Sample from a Wishart distribution

DPMGibbsSkewT_parallel

Slice Sampling of Dirichlet Process Mixture of skew Students's t-distibutions

MLE for Gamma distribution

C++ implementation of multivariate normal likelihood function for multiple inputs

vclust2mcoclustC

C++ implementation

cluster_est_Fmeasure

Point estimate of the partition using the F-measure as the cost function.

Sample from a Normal inverse-Wishart distribution whose parameter are given by the structure hyper

C++ implementation of multivariate skew Normal probability density function for multiple inputs

Construction of an Empirical based prior

C++ implementation of multivariate skew normal likelihood function for multiple inputs

Compute a limited F-measure

print.summaryDPMMclust

Methods for a summary of a 'DPMMclust' object

Slice Sampling of Dirichlet Process Mixture of skew normal ditributions

Return updated sufficient statistics S for skew t-distribution with data matrix z

multivariate Normal inverse Wishart probability density function for multiple inputs

Slice Sampling of Dirichlet Process Mixture of skew Students's t-distibutions

Multivariate log gamma function

Plot of a Dirichlet process mixture of skew normal distribution partition

Multiple cost computations with Fmeasure as the loss function

C++ implementation of multivariate Normal probability density function for multiple inputs

Return updated sufficient statistics S with new data matrix z

C++ implementation of multivariate Normal probability density function for multiple inputs

C++ implementation of the F-measure computation without the ref classe 0

DPMGibbsN_parallel

Slice Sampling of the Dirichlet Process Mixture Model with a prior on alpha

DPMGibbsSkewT_SeqPrior

Slice Sampling of Dirichlet Process Mixture of skew Students's t-distibutions

EM MLE for mixture of sNiW

Bayesian Nonparametrics for Automatic Gating of Flow Cytometry data

Probability density function of multiple structured Normal inverse Wishart

Scatterplot of flow cytometry data

Plot of a Dirichlet process mixture of skew t-distribution partition

C++ implementation of multivariate Normal probability density function for multiple inputs

multivariate Student's t-distribution probability density function

C++ implementation of multivariate Normal probability density function for multiple inputs

Sampler for the concentration parameter of a Dirichlet process

Generating cluster data from the Chinese Restaurant Process

C++ implementation

cluster_est_pear

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

DPMGibbsN_SeqPrior

Slice Sampling of Dirichlet Process Mixture of Gaussian distibutions

DPMGibbsSkewN_parallel

Parallel Implementation of Slice Sampling of Dirichlet Process Mixture of skew Normals

C++ implementation of the F-measure computation

C++ implementation of similarity matrix computation using precomputed distances

ELoss of a partition point estimate compared to a gold standard

C++ implementation of the multinomial sampling from a matrix of column vectors, each containing the sampling probabilities for their respective draw

similarityMat_nocostC

C++ implementation

Slice Sampling of the Dirichlet Process Mixture Model with a prior on alpha

Sample from a inverse-Wishart distribution

Burning MCMC iterations from a Dirichlet Process Mixture Model.

C++ implementation of multivariate structured Normal inverse Wishart probability density function for multiple inputs

MLE for sNiW distributed observations

Return updated sufficient statistics S with new data matrix z

C++ implementation of multivariate skew t likelihood function for multiple inputs

Computes the coclustering (or similarity) matrix

Sample from a normal inverse Wishart distribution whose parameter are given by the structure SufStat

cluster_est_binder

Point estimate of the partition for the Binder loss function

summary.DPMMclust

Summarizing Dirichlet Process Mixture Models