update_Omega

Function to sequentially update the value of <code>Omega</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

update_Omega function

<dl><dt>Omega_old</dt>
<dd>The old (previous) value of <code>Omega</code>.</dd>
<dt>lambda_old</dt>
<dd>The value of the forgetting factor.</dd>
<dt>m_new</dt>
<dd>The new (current) value of <code>m</code>.</dd></dl>

Arguments

Update Omega — update_Omega

<dl>

<dt>Omega_old</dt>
<dd>The old (previous) value of <code>Omega</code>.</dd>


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


<dt>m_new</dt>
<dd>The new (current) value of <code>m</code>.</dd>

</dl>

update_Omega: Update `Omega`

Description

Usage

Arguments