make_matrices

Fitting hidden Markov models using automatic differentiation
and Laplace approximation, allowing for fast inference and flexible covariate
effects (including random effects and smoothing splines) on model parameters.
The package is described by Michelot (2022) <doi:10.48550/arXiv.2211.14139>.

Theo Michelot

hmmTMB

Fit Hidden Markov Models using Template Model Builder

Richard Glennie

make_matrices function

<dl><dt>formulas</dt>
<dd>List of formulas (possibly nested, e.g. for use within Observation)</dd>
<dt>data</dt>
<dd>Data frame including covariates</dd>
<dt>new_data</dt>
<dd>Optional new data set, including covariates for which
the design matrices should be created. This needs to be passed in addition
to the argument '<code>data</code>', for cases where smooth terms or factor
covariates are included, and the original data set is needed to determine
the full range of covariate values.</dd>
<dt>gam_args</dt>
<dd>Named list of additional arguments for <code><a href="/link/mgcv%3A%3Agam()?package=hmmTMB&version=1.1.0" data-mini-rdoc="hmmTMB::mgcv::gam()">mgcv::gam()</a></code>,
such as knots.</dd></dl>

Arguments

Create model matrices — make_matrices

<dl>

<dt>formulas</dt>
<dd>List of formulas (possibly nested, e.g. for use within Observation)</dd>


<dt>data</dt>
<dd>Data frame including covariates</dd>


<dt>new_data</dt>
<dd>Optional new data set, including covariates for which
the design matrices should be created. This needs to be passed in addition
to the argument '<code>data</code>', for cases where smooth terms or factor
covariates are included, and the original data set is needed to determine
the full range of covariate values.</dd>


<dt>gam_args</dt>
<dd>Named list of additional arguments for <code><a href='https://rdrr.io/pkg/mgcv/man/gam.html'>mgcv::gam()</a></code>,
such as knots.</dd>

</dl>

make_matrices: Create model matrices

Description

Usage

Value

Arguments