create_scaling

Interpretable nonparametric modeling of longitudinal data
using additive Gaussian process regression. Contains functionality
for inferring covariate effects and assessing covariate relevances.
Models are specified using a convenient formula syntax, and can include
shared, group-specific, non-stationary, heterogeneous and temporally
uncertain effects. Bayesian inference for model parameters is performed
using 'Stan'. The modeling approach and methods are described in detail in
Timonen et al. (2021) <doi:10.1093/bioinformatics/btab021>.

Juho Timonen

lgpr

Longitudinal Gaussian Process Regression

Andrew Johnson

create_scaling function

<dl><dt>x</dt>
<dd>variable measurements (might contain <code>NA</code> or <code>NaN</code>)</dd>
<dt>name</dt>
<dd>variable name</dd></dl>

Arguments

Create a standardizing transform — create_scaling

<dl>

<dt>x</dt>
<dd>variable measurements (might contain <code>NA</code> or <code>NaN</code>)</dd>


<dt>name</dt>
<dd>variable name</dd>

</dl>

create_scaling: Create a standardizing transform

Description

Usage

Value

Arguments

See Also