sampleClassC

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

internal

Dirichlet process mixture of multivariate normal, skew normal or skew t-distributions
modeling oriented towards flow-cytometry data preprocessing applications. Method is
detailed in: Hejblum, Alkhassimn, Gottardo, Caron & Thiebaut (2019) <doi: 10.1214/18-AOAS1209>.

Boris P. Hejblum

NPflow

Bayesian Nonparametrics for Automatic Gating of Flow-Cytometry
Data

Chariff Alkhassim

Francois Caron

sampleClassC function

<dl><dt>probMat</dt>
<dd>a numeric matrix of dim <code>k x n</code> of containing column vectors of sampling
probabilities for each class <code>k</code>.</dd>
<dt>Log</dt>
<dd>a logical flag indicating whether the provided <code>probMat</code> is on the log scale
or natural probability scale. Default is <code>FALSE</code> in which case it is considered on the natural
probability scale.</dd></dl>

Arguments

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

<dl>

<dt>probMat</dt>
<dd>a numeric matrix of dim <code>k x n</code> of containing column vectors of sampling
probabilities for each class <code>k</code>.</dd>


<dt>Log</dt>
<dd>a logical flag indicating whether the provided <code>probMat</code> is on the log scale
or natural probability scale. Default is <code>FALSE</code> in which case it is considered on the natural
probability scale.</dd>

</dl>

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

Description

Usage

Value

Arguments