.hess_to_cov

helper function to safely convert a Hessian matrix to covariance matrix

internal

Flexible parametric models for time-to-event data,
including the Royston-Parmar spline model, generalized gamma and
generalized F distributions.  Any user-defined parametric
distribution can be fitted, given at least an R function defining
the probability density or hazard. There are also tools for
fitting and predicting from fully parametric multi-state models,
based on either cause-specific hazards or mixture models.

Christopher Jackson

flexsurv

Flexible Parametric Survival and Multi-State Models

Paul Metcalfe

Jordan Amdahl

Matthew T. Warkentin

Michael Sweeting

Kevin Kunzmann

.hess_to_cov function

<dl><dt>hessian</dt>
<dd>hessian matrix to convert to covariance matrix (must be evaluated at MLE)</dd>
<dt>tol.solve</dt>
<dd>tolerance used for solve()</dd>
<dt>tol.evalues</dt>
<dd>accepted tolerance for negative eigenvalues of the covariance matrix</dd>
<dt>...</dt>
<dd>arguments passed to Matrix::nearPD</dd></dl>

Arguments

helper function to safely convert a Hessian matrix to covariance matrix — .hess_to_cov

<dl>

<dt>hessian</dt>
<dd>hessian matrix to convert to covariance matrix (must be evaluated at MLE)</dd>


<dt>tol.solve</dt>
<dd>tolerance used for solve()</dd>


<dt>tol.evalues</dt>
<dd>accepted tolerance for negative eigenvalues of the covariance matrix</dd>


<dt>...</dt>
<dd>arguments passed to Matrix::nearPD</dd>

</dl>

.hess_to_cov: helper function to safely convert a Hessian matrix to covariance matrix

Description

Usage

Arguments