rbern

#' <a href="https://lifecycle.r-lib.org/articles/stages.html#stable"><img src="figures/lifecycle-stable.svg?package=causact&version=0.5.7" alt="[Stable]"></a>
Density, distribution function, quantile function and random generation for the benoulli distribution with parameter <code>prob</code>.

Accelerate Bayesian analytics workflows in 'R' through interactive modelling,
visualization, and inference. Define probabilistic graphical models using directed
acyclic graphs (DAGs) as a unifying language for business stakeholders, statisticians,
and programmers. This package relies on interfacing with the 'numpyro' python package.

Adam Fleischhacker

causact

Fast, Easy, and Visual Bayesian Inference

Daniela Dapena

Rose Nguyen

Jared Sharpe

rbern function

<dl><dt>n</dt>
<dd>number of observations. If <code>length(n) &gt; 1</code>, the length is taken to be the number required.</dd>
<dt>prob</dt>
<dd>probability of success of each trial</dd></dl>

Arguments

#' <a href='https://lifecycle.r-lib.org/articles/stages.html#stable'><img src='figures/lifecycle-stable.svg' alt='[Stable]' /></a>
Density, distribution function, quantile function and random generation for the benoulli distribution with parameter <code>prob</code>.

The Bernoulli Distribution — rbern

<dl>

<dt>n</dt>
<dd>number of observations. If <code>length(n) &gt; 1</code>, the length is taken to be the number required.</dd>


<dt>prob</dt>
<dd>probability of success of each trial</dd>

</dl>

rbern: The Bernoulli Distribution

Description

Usage

Value

Arguments

Examples