dinvgamma_stanlike

qinvgamma_stanlike

Using the same parametrization as Stan. More info
<a href="https://mc-stan.org/docs/2_24/functions-reference/inverse-gamma-distribution.html">here</a>.

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

dinvgamma_stanlike function

<dl><dt>x</dt>
<dd>point where to compute the density</dd>
<dt>alpha</dt>
<dd>positive real number</dd>
<dt>beta</dt>
<dd>positive real number</dd>
<dt>log</dt>
<dd>is log-scale used?</dd>
<dt>p</dt>
<dd>quantile (must be between 0 and 1)</dd></dl>

Arguments

Using the same parametrization as Stan. More info
<a href='https://mc-stan.org/docs/2_24/functions-reference/inverse-gamma-distribution.html'>here</a>.

Density and quantile functions of the inverse gamma distribution — dinvgamma_stanlike

<dl>

<dt>x</dt>
<dd>point where to compute the density</dd>


<dt>alpha</dt>
<dd>positive real number</dd>


<dt>beta</dt>
<dd>positive real number</dd>


<dt>log</dt>
<dd>is log-scale used?</dd>


<dt>p</dt>
<dd>quantile (must be between 0 and 1)</dd>

</dl>

dinvgamma_stanlike: Density and quantile functions of the inverse gamma distribution

Description

Usage

Value

Arguments

See Also