computeTwoSidedPvalueR

Wrapper function to compute a two-sided p-value from a statistics
or quantile value, with the aid of lower and upper bounds.

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

computeTwoSidedPvalueR function

<dl><dt>x</dt>
<dd>The two-sided p-value to be converted.</dd>
<dt>a</dt>
<dd>The lower bound, e.g. 0.</dd>
<dt>b</dt>
<dd>The upper bound, e.g. 2.</dd></dl>

Arguments

Compute a two-sided p-value from a quantile value — computeTwoSidedPvalueR

<dl>

<dt>x</dt>
<dd>The two-sided p-value to be converted.</dd>


<dt>a</dt>
<dd>The lower bound, e.g. 0.</dd>


<dt>b</dt>
<dd>The upper bound, e.g. 2.</dd>

</dl>

computeTwoSidedPvalueR: Compute a two-sided p-value from a quantile value

Description

Usage

Arguments