make_matrices

Build the design matrix and the penalty matrix for models involving penalised splines based on a formula and a data set

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

make_matrices function

<dl><dt>formula</dt>
<dd>right side of a formula as used in <code>mgcv</code></dd>
<dt>data</dt>
<dd>data frame containing the variables in the formula</dd>
<dt>knots</dt>
<dd>optional list containing user specified knot values to be used for basis construction
For most bases the user simply supplies the knots to be used, which must match up with the k value supplied (note that the number of knots is not always just k).
See <code>mgcv</code> documentation for more details.</dd></dl>

Arguments

Build the design matrix and the penalty matrix for models involving penalised splines based on a formula and a data set — make_matrices

<dl>

<dt>formula</dt>
<dd>right side of a formula as used in <code>mgcv</code></dd>


<dt>data</dt>
<dd>data frame containing the variables in the formula</dd>


<dt>knots</dt>
<dd>optional list containing user specified knot values to be used for basis construction
For most bases the user simply supplies the knots to be used, which must match up with the k value supplied (note that the number of knots is not always just k).
See <code>mgcv</code> documentation for more details.</dd>

</dl>

make_matrices: Build the design matrix and the penalty matrix for models involving penalised splines based on a formula and a data set

Description

Usage

Value

Arguments

Examples