klDiv

Computes the Kullback-Leibler divergence for two
arbitrary densities f1 and f2.

When using pooled p-values to adjust for multiple testing, there is an inherent balance that must be struck between rejection based on weak evidence spread among many tests and strong evidence in a few, explored in Salahub and Olford (2023) <arXiv:2310.16600>. This package provides functionality to compute marginal and central rejection levels and the centrality quotient for p-value pooling functions and provides implementations of the chi-squared quantile pooled p-value (described in Salahub and Oldford (2023)) and a proposal from Heard and Rubin-Delanchy (2018) <doi:10.1093/biomet/asx076> to control the quotient's value.

Chris Salahub

PoolBal

Balancing Central and Marginal Rejection of Pooled p-Values

klDiv function

<dl><dt>f1</dt>
<dd>density function of a real-valued random variable</dd>
<dt>f2</dt>
<dd>density function of a real-values random variable</dd>
<dt>lower</dt>
<dd>real value, the lower bound of integration</dd>
<dt>upper</dt>
<dd>real value, the upper bound of integration</dd></dl>

Arguments

Author

Compute the Kullback-Leibler divergence — klDiv

<dl>

<dt>f1</dt>
<dd>density function of a real-valued random variable</dd>


<dt>f2</dt>
<dd>density function of a real-values random variable</dd>


<dt>lower</dt>
<dd>real value, the lower bound of integration</dd>


<dt>upper</dt>
<dd>real value, the upper bound of integration</dd>

</dl>

klDiv: Compute the Kullback-Leibler divergence

Description

Usage

Value

Arguments

Author

Details

Examples