get_xbar_deriv

Function to compute the derivative of <code>xbar</code>.

internal

An implementation of the adaptive forgetting factor scheme described in Bodenham and Adams (2016) <doi:10.1007/s11222-016-9684-8> which adaptively estimates the mean and variance of a stream in order to detect multiple changepoints in streaming data. The implementation is in 'C++' and uses 'Rcpp'. Additionally, implementations of the fixed forgetting factor scheme from the same paper, as well as the classic cumulative sum ('CUSUM') and exponentially weighted moving average ('EWMA') methods, are included.

Dean Bodenham

ffstream

Forgetting Factor Methods for Change Detection in Streaming Data

get_xbar_deriv function

<dl><dt>Delta</dt>
<dd>The value of <code>Delta</code>, the derivative of <code>m</code>.</dd>
<dt>xbar</dt>
<dd>The value of the forgetting factor mean.</dd>
<dt>Omega</dt>
<dd>The value of <code>Omega</code>, the derivative of <code>w</code>.</dd>
<dt>w</dt>
<dd>The value of the weight used to compute <code>xbar</code>.</dd></dl>

Arguments

Compute the derivative of xbar — get_xbar_deriv

<dl>

<dt>Delta</dt>
<dd>The value of <code>Delta</code>, the derivative of <code>m</code>.</dd>


<dt>xbar</dt>
<dd>The value of the forgetting factor mean.</dd>


<dt>Omega</dt>
<dd>The value of <code>Omega</code>, the derivative of <code>w</code>.</dd>


<dt>w</dt>
<dd>The value of the weight used to compute <code>xbar</code>.</dd>

</dl>

get_xbar_deriv: Compute the derivative of `xbar`

Description

Usage

Arguments