This function computes the probability density function (pdf) of a univariate distribution

Inference, goodness-of-fit tests, and predictions for continuous and discrete univariate Hidden Markov Models (HMM), including zero-inflated distributions. The goodness-of-fit test is based on a Cramer-von Mises statistic and uses parametric bootstrap to estimate the p-value. The description of the methodology is taken from Nasri et al (2020) <doi:10.1029/2019WR025122>.

Bouchra Nasri

GenHMM1d

Goodness-of-Fit for Zero-Inflated Univariate Hidden Markov
Models

Mamadou Yamar Thioub

PDF function

<dl><dt>family</dt>
<dd>distribution name; run the function distributions() for help</dd>
<dt>y</dt>
<dd>observations</dd>
<dt>param</dt>
<dd>parameters of the distribution; (1 x p)</dd>
<dt>size</dt>
<dd>additional parameter for some discrete distributions; run the command distributions() for help</dd></dl>

Arguments

Probability density function — PDF

<dl>

<dt>family</dt>
<dd>distribution name; run the function distributions() for help</dd>


<dt>y</dt>
<dd>observations</dd>


<dt>param</dt>
<dd>parameters of the distribution; (1 x p)</dd>


<dt>size</dt>
<dd>additional parameter for some discrete distributions; run the command distributions() for help</dd>

</dl>

PDF: Probability density function

Description

Usage

Value

Arguments