nearestPD

This function first check if a matrix A is positive definite, typically a correlation or variance-covariance matrix.
If A is not positive definite, this function computes the nearest positive definite matrix of A using the function nearPD from package Matrix.

This is a stochastic framework that combines biochemical reaction networks with extended Kalman filter and Rauch-Tung-Striebel smoothing.
This framework allows to investigate the dynamics of cell differentiation from high-dimensional clonal tracking data subject to measurement noise, false negative errors, and systematically unobserved cell types.
Our tool can provide statistical support to biologists in gene therapy clonal tracking studies for a deeper understanding of clonal reconstitution dynamics. Further details on the methods can be found in L. Del Core et al., (2022) <doi:10.1101/2022.07.08.499353>.

Luca Del Core

Karen

Kalman Reaction Networks

Danilo Pellin

Marco Grzegorczyk

Ernst Wit

nearestPD function

<dl><dt>A</dt>
<dd>numeric \(n \times n\) approximately positive definite matrix, typically an approximation to a correlation or covariance matrix.
If A is not symmetric (and ensureSymmetry is not false), symmpart(A) is used.</dd>
<dt>...</dt>
<dd>Further arguments to be passed to nearPD (see package Matrix for details).</dd></dl>

Arguments

Nearest Positive Definite Matrix — nearestPD

<dl>

<dt>A</dt>
<dd>numeric \(n \times n\) approximately positive definite matrix, typically an approximation to a correlation or covariance matrix.
If A is not symmetric (and ensureSymmetry is not false), symmpart(A) is used.</dd>


<dt>...</dt>
<dd>Further arguments to be passed to nearPD (see package Matrix for details).</dd>

</dl>

nearestPD: Nearest Positive Definite Matrix

Description

Usage

Value

Arguments

Examples