gradient

This function calculates the gradient of a function, numerically, including the possibility
of doing it in parallel.

General optimisation and specific tools for the parameter estimation (i.e. calibration) of complex models, including stochastic ones. It implements generic functions that can be used for fitting any type of models, especially those with non-differentiable objective functions, with the same syntax as base::optim.
It supports multiple phases estimation (sequential parameter masking), constrained optimization (bounding box restrictions) and automatic parallel computation of numerical gradients.
Some common maximum likelihood estimation methods and automated construction of the objective function from simulated model outputs is provided.
See <https://roliveros-ramos.github.io/calibrar/> for more details.

Ricardo OliverosRamos

calibrar

Automated Parameter Estimation for Complex Models

Ricardo Oliveros-Ramos

gradient function

<dl><dt>fn</dt>
<dd>The function to calculate the gradient.</dd>
<dt>x</dt>
<dd>The value to compute the gradient at.</dd>
<dt>method</dt>
<dd>The method used. Currently implemented: central, backward, forward and Richardson. See details.</dd>
<dt>control</dt>
<dd>A list of control arguments.</dd>
<dt>parallel</dt>
<dd>Boolean, should numerical derivatives be calculated in parallel?</dd>
<dt>...</dt>
<dd>Additional arguments to be passed to <code>fn</code>.</dd></dl>

Arguments

Numerical computation of the gradient, with parallel capabilities — gradient

<dl>

<dt>fn</dt>
<dd>The function to calculate the gradient.</dd>


<dt>x</dt>
<dd>The value to compute the gradient at.</dd>


<dt>method</dt>
<dd>The method used. Currently implemented: central, backward, forward and Richardson. See details.</dd>


<dt>control</dt>
<dd>A list of control arguments.</dd>


<dt>parallel</dt>
<dd>Boolean, should numerical derivatives be calculated in parallel?</dd>


<dt>...</dt>
<dd>Additional arguments to be passed to <code>fn</code>.</dd>

</dl>

gradient: Numerical computation of the gradient, with parallel capabilities

Description

Usage

Value

Arguments

Examples