print.compatibility_check

Print method for compatibility_check

Implements conjugate power priors for efficient Bayesian analysis
of normal data. Power priors allow principled incorporation of historical
information while controlling the degree of borrowing through a discounting
parameter (Ibrahim and Chen (2000) <doi:10.1214/ss/1009212519>). This
package provides closed-form conjugate representations for both univariate
and multivariate normal data using Normal-Inverse-Chi-squared and
Normal-Inverse-Wishart distributions, eliminating the need for MCMC sampling.
The conjugate framework builds upon standard Bayesian methods described in
Gelman et al. (2013, ISBN:978-1439840955).

Yusuke Yamaguchi

powerprior

Conjugate Power Priors for Bayesian Analysis of Normal Data

Yifei Huang

print.compatibility_check function

<dl><dt>x</dt>
<dd>Object of class "compatibility_check"</dd>
<dt>digits</dt>
<dd>Number of digits to round to</dd>
<dt>...</dt>
<dd>Additional arguments</dd></dl>

Arguments

Print method for compatibility_check — print.compatibility_check

<dl>

<dt>x</dt>
<dd>Object of class "compatibility_check"</dd>


<dt>digits</dt>
<dd>Number of digits to round to</dd>


<dt>...</dt>
<dd>Additional arguments</dd>

</dl>

print.compatibility_check: Print method for compatibility_check

Description

Usage

Value

Arguments