postmode

postmode.fevd

Calculate the posterior mode from an MCMC sample for &#8220;fevd&#8221; objects.

 methods 

 optimize 

General functions for performing extreme value analysis. In particular, allows for inclusion of covariates into the parameters of the extreme-value distributions, as well as estimation through MLE, L-moments, generalized (penalized) MLE (GMLE), as well as Bayes. Inference methods include parametric normal approximation, profile-likelihood, Bayes, and bootstrapping. Some bivariate functionality and dependence checking (e.g., auto-tail dependence function plot, extremal index estimation) is also included. For a tutorial, see Gilleland and Katz (2016) <doi: 10.18637/jss.v072.i08> and for bootstrapping, please see Gilleland (2020) <doi: 10.1175/JTECH-D-20-0070.1>.

Eric Gilleland

extRemes

Extreme Value Analysis

postmode function

<dl> <dt>x</dt>
<dd>An object of class &#8220;fevd&#8221; where component <code>method</code> = &#8220;Bayesian&#8221;.</dd>
 <dt>burn.in</dt>
<dd>The furst burn.in samples from the posterior distribution will be removed before calculation.</dd> <dt>verbose</dt>
<dd>logical, should progress information be printed to the screen.</dd> <dt>...</dt>
<dd>Not used.</dd></dl>

Arguments

Author

Posterior Mode from an MCMC Sample — postmode

<dl>

 <dt>x</dt>
<dd>An object of class &#8220;fevd&#8221; where component <code>method</code> = &#8220;Bayesian&#8221;.</dd>


 <dt>burn.in</dt>
<dd>The furst burn.in samples from the posterior distribution will be removed before calculation.</dd>

 <dt>verbose</dt>
<dd>logical, should progress information be printed to the screen.</dd>

 <dt>...</dt>
<dd>Not used.</dd>

</dl>

postmode: Posterior Mode from an MCMC Sample

Description

Usage

Value

Arguments

Author

Details

See Also

Examples