nessi

A small group of researchers managed to put an accelerometer on the Loch Ness Monster and collected data for a few days. 
Now we have a data set of the overall dynamic body acceleration (ODBA) of the creature.

datasets

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 Koslik

LaMa

Fast Numerical Maximum Likelihood Estimation for Latent Markov
Models

nessi function

A data frame with 5.000 rows and 3 variables:<dl>
 <dt>ODBA</dt>
<dd>overall dynamci body acceleration</dd> <dt>logODBA</dt>
<dd>logarithm of overall dynamic body acceleration</dd> <dt>state</dt>
<dd>hidden state variable</dd>
</dl>

Format

Loch Ness Monster Acceleration Data — nessi

A data frame with 5.000 rows and 3 variables:<dl>
 <dt>ODBA</dt>
<dd>overall dynamci body acceleration</dd>

 <dt>logODBA</dt>
<dd>logarithm of overall dynamic body acceleration</dd>

 <dt>state</dt>
<dd>hidden state variable</dd>


</dl>

nessi: Loch Ness Monster Acceleration Data

Description

Usage

Arguments

Format