get_L_deriv

Compute the derivative of the cost function L, defined as:
$$L_{N} = [\bar{x}_{N-1, \overrightarrow{\lambda}} - x_{N}$$

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_L_deriv function

<dl><dt>xbar_old</dt>
<dd>The old (previous) value of <code>xbar</code>.</dd>
<dt>xbar_old_deriv</dt>
<dd>The old (previous) value of the derivative
of <code>xbar</code>.</dd>
<dt>x_new</dt>
<dd>The value of the current observation.</dd></dl>

Arguments

Compute the derivative of L — get_L_deriv

<dl>

<dt>xbar_old</dt>
<dd>The old (previous) value of <code>xbar</code>.</dd>


<dt>xbar_old_deriv</dt>
<dd>The old (previous) value of the derivative
of <code>xbar</code>.</dd>


<dt>x_new</dt>
<dd>The value of the current observation.</dd>

</dl>

get_L_deriv: Compute the derivative of L

Description

Usage

Arguments