getCovNames

Get names of any covariates used in probability distribution parameters

Extended tools for analyzing telemetry data using generalized hidden Markov models. Features of momentuHMM (pronounced ``momentum'') include data pre-processing and visualization, fitting HMMs to location and auxiliary biotelemetry or environmental data, biased and correlated random walk movement models, hierarchical HMMs, multiple imputation for incorporating location measurement error and missing data, user-specified design matrices and constraints for covariate modelling of parameters, random effects, decoding of the state process, visualization of fitted models, model checking and selection, and simulation. See McClintock and Michelot (2018) <doi:10.1111/2041-210X.12995>.

Brett T McClintock

momentuHMM

Maximum Likelihood Analysis of Animal Movement Behavior Using
Multivariate Hidden Markov Models

getCovNames function

<dl><dt>m</dt>
<dd><code>momentuHMM</code> object</dd>
<dt>p</dt>
<dd>list returned by <code>parDef</code></dd>
<dt>distname</dt>
<dd>Name of the data stream</dd></dl>

Arguments

Get names of any covariates used in probability distribution parameters — getCovNames

<dl>

<dt>m</dt>
<dd><code>momentuHMM</code> object</dd>


<dt>p</dt>
<dd>list returned by <code>parDef</code></dd>


<dt>distname</dt>
<dd>Name of the data stream</dd>

</dl>

getCovNames: Get names of any covariates used in probability distribution parameters

Description

Usage

Value

Arguments