warp_input

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

warp_input function

<dl><dt>x</dt>
<dd>a vector of length \(n\)</dd>
<dt>a</dt>
<dd>steepness of the warping function rise</dd></dl>

Arguments

Input warping function — warp_input

<dl>

<dt>x</dt>
<dd>a vector of length \(n\)</dd>


<dt>a</dt>
<dd>steepness of the warping function rise</dd>

</dl>

warp_input: Input warping function

Description

Usage

Value

Arguments

See Also