model_summary

param_summary

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

model_summary function

<dl><dt>object</dt>
<dd>a model or fit</dd>
<dt>digits</dt>
<dd>number of digits to round floats to</dd></dl>

Arguments

Print a model summary. — model_summary

<dl>

<dt>object</dt>
<dd>a model or fit</dd>


<dt>digits</dt>
<dd>number of digits to round floats to</dd>

</dl>

model_summary: Print a model summary.

Description

Usage

Value

Arguments