R: C++ implementation of multivariate Normal inverse Wishart...

mmNiWpdfC {NPflow} | R Documentation

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

### Description

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

### Usage

    mmNiWpdfC(Mu, Sigma, U_Mu0, U_Kappa0, U_Nu0, U_Sigma0, Log = TRUE)

### Arguments

`Mu` | data matrix of dimension `p x n`, `p` being the dimension of the data and n the number of data points, where each column is an observed mean vector.
`Sigma` | list of length `n` of observed variance-covariance matrices, each of dimensions `p x p`.
`U_Mu0` | mean vectors matrix of dimension `p x K`, `K` being the number of distributions for which the density probability has to be evaluated
`U_Kappa0` | vector of length `K` of scale parameters.
`U_Nu0` | vector of length `K` of degree of freedom parameters.
`U_Sigma0` | list of length `K` of variance-covariance matrices, each of dimensions `p x p`.
`Log` | logical flag for returning the log of the probability density function. Defaults is `TRUE`.

### Value

matrix of densities of dimension K x n

### References

Hejblum BP, Alkhassim C, Gottardo R, Caron F and Thiebaut R (2019) Sequential Dirichlet Process Mixtures of Multivariate Skew t-distributions for Model-based Clustering of Flow Cytometry Data. The Annals of Applied Statistics, 13(1): 638-660. <doi: 10.1214/18-AOAS1209>. <arXiv: 1702.04407>. https://arxiv.org/abs/1702.04407 https://doi.org/10.1214/18-AOAS1209

[Package NPflow version 0.13.3 Index]