sdreport_outer

Computes standard deviations for the smoothing parameters of a model object returned by <code>qreml</code> using the delta method.

A variety of latent Markov models, including hidden Markov models, hidden semi-Markov models,
state-space models and continuous-time variants can be formulated and estimated within the same framework via directly maximising the likelihood function using the so-called forward algorithm.
Applied researchers often need custom models that standard software does not easily support.
Writing tailored 'R' code offers flexibility but suffers from slow estimation speeds.
We address these issues by providing easy-to-use functions (written in 'C++' for speed) for common tasks like the forward algorithm.
These functions can be combined into custom models in a Lego-type approach, offering up to 10-20 times faster estimation via standard numerical optimisers.
To aid in building fully custom likelihood functions, several vignettes are included that show how to simulate data from and estimate all the above model classes.

Jan-Ole Fischer

LaMa

Fast Numerical Maximum Likelihood Estimation for Latent Markov
Models

sdreport_outer function

<dl><dt>mod</dt>
<dd>model object as returned by <code>qreml</code></dd>
<dt>invert</dt>
<dd>optional logical; if <code>TRUE</code>, the inverse smoothing parameters (variances) are returned along with the transformed standard deviations obtained via the delta method.</dd></dl>

Arguments

Report uncertainty of the estimated smoothing parameters or variances — sdreport_outer

<dl>

<dt>mod</dt>
<dd>model object as returned by <code>qreml</code></dd>


<dt>invert</dt>
<dd>optional logical; if <code>TRUE</code>, the inverse smoothing parameters (variances) are returned along with the transformed standard deviations obtained via the delta method.</dd>

</dl>

sdreport_outer: Report uncertainty of the estimated smoothing parameters or variances

Description

Usage

Value

Arguments

Details

Examples