get_density

internal

Flexible and informed regression with Multiple Change Points. 'mcp' can infer change points in means, variances, autocorrelation structure, and any combination of these, as well as the parameters of the segments in between. All parameters are estimated with uncertainty and prediction intervals are supported - also near the change points. 'mcp' supports hypothesis testing via Savage-Dickey density ratio, posterior contrasts, and cross-validation. 'mcp' is described in Lindeløv (submitted) <doi:10.31219/osf.io/fzqxv> and generalizes the approach described in Carlin, Gelfand, & Smith (1992) <doi:10.2307/2347570> and Stephens (1994) <doi:10.2307/2986119>.

Jonas Kristoffer Lindel<c3><b8>v

Regression with Multiple Change Points

Jonas Kristoffer Lindeløv

get_density function

<dl><dt>samples</dt>
<dd>An mcmc.list</dd>
<dt>LHS</dt>
<dd>Expression to compute posterior</dd>
<dt>value</dt>
<dd>What value to evaluate the density at</dd></dl>

Arguments

Jonas Kristoffer Lindeløv <a href="/link/jonas%40lindeloev.dk?package=mcp&version=0.3.3" data-mini-rdoc="mcp::jonas@lindeloev.dk">jonas@lindeloev.dk</a>

Author

Compute the density at a specific point. — get_density

<dl>

<dt>samples</dt>
<dd>An mcmc.list</dd>


<dt>LHS</dt>
<dd>Expression to compute posterior</dd>


<dt>value</dt>
<dd>What value to evaluate the density at</dd>

</dl>

Jonas Kristoffer Lindeløv <a href='mailto:jonas@lindeloev.dk'>jonas@lindeloev.dk</a>

get_density: Compute the density at a specific point.

Description

Usage

Value

Arguments

Author