sum_every_n

Use frequentist and Bayesian methods to estimate parameters from a
binary outcome misclassification model. These methods correct for the problem
of "label switching" by assuming that the sum of outcome sensitivity and
specificity is at least 1. A description of the analysis methods is
available in Hochstedler and Wells (2023) <doi:10.48550/arXiv.2303.10215>.

Kimberly Hochstedler

COMBO

Correcting Misclassified Binary Outcomes in Association Studies

Kimberly Hochstedler Webb

sum_every_n function

<dl><dt>x</dt>
<dd>A numeric vector to sum over</dd>
<dt>n</dt>
<dd>A numeric value specifying the distance between the reference index and the next index to be summed</dd></dl>

Arguments

Sum Every "n"th Element — sum_every_n

<dl>

<dt>x</dt>
<dd>A numeric vector to sum over</dd>


<dt>n</dt>
<dd>A numeric value specifying the distance between the reference index and the next index to be summed</dd>

</dl>

sum_every_n: Sum Every "n"th Element

Description

Usage

Value

Arguments